{"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras\n\nTime series prediction problems are a difficult type of predictive modeling problem.\n\nUnlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables.\n\nA powerful type of neural network designed to handle sequence dependence is called a recurrent neural network. The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in deep learning because very large architectures can be successfully trained.\n\nIn this post, you will discover how to develop LSTM networks in Python using the Keras deep learning library to address a demonstration time-series prediction problem.\n\nAfter completing this tutorial, you will know how to implement and develop LSTM networks for your own time series prediction problems and other more general sequence problems. You will know:\n\n*   About the International Airline Passengers time-series prediction problem\n*   How to develop LSTM networks for regression, window, and time-step-based framing of time series prediction problems\n*   How to develop and make predictions using LSTM networks that maintain state (memory) across very long sequences\n\nIn this tutorial, we will develop a number of LSTMs for a standard time series prediction problem. The problem and the chosen configuration for the LSTM networks are for demonstration purposes only; they are not optimized.\n\nThese examples will show exactly how you can develop your own differently structured LSTM networks for time series predictive modeling problems.","metadata":{"id":"iPfsr2ubz_cf"}},{"cell_type":"markdown","source":"\n# Problem Description\n>The problem you will look at in this post is the International Airline Passengers prediction problem.\n\n>his is a problem where, given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. The data ranges from January 1949 to December 1960, or 12 years, with 144 observations.\n\n>[Download the dataset](https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv) (save as “airline-passengers.csv“).\nBelow is a sample of the first few lines of the file.\n\n\n```\n\"Month\",\"Passengers\"\n\"1949-01\",112\n\"1949-02\",118\n\"1949-03\",132\n\"1949-04\",129\n\"1949-05\",121\n```\n\n\n>You can load this dataset easily using the Pandas library. You are not interested in the date, given that each observation is separated by the same interval of one month. Therefore, when you load the dataset, you can exclude the first column.","metadata":{"id":"OOnkbcFJ0NAZ"}},{"cell_type":"code","source":"import pandas\nimport matplotlib.pyplot as plt\ndataset = pandas.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', usecols=[1], engine='python')\nplt.plot(dataset)\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"2NAKmS3oz88y","outputId":"0fe9164a-f15c-472a-fe64-2290786629b7","execution":{"iopub.status.busy":"2023-11-01T09:59:35.121995Z","iopub.execute_input":"2023-11-01T09:59:35.122619Z","iopub.status.idle":"2023-11-01T09:59:35.314001Z","shell.execute_reply.started":"2023-11-01T09:59:35.122584Z","shell.execute_reply":"2023-11-01T09:59:35.312789Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"markdown","source":">You can see an upward trend in the dataset over time. You can also see some periodicity in the dataset that probably corresponds to the Northern Hemisphere vacation period.\n\n>You can phrase the problem as a regression problem. That is, given the number of passengers (in units of thousands) this month, what is the number of passengers next month?\n\n","metadata":{"id":"VUyjZEHo2xhL"}},{"cell_type":"markdown","source":"# Data Preparation and librairies importing\n\n\n\n\n\n","metadata":{"id":"BCYd6kmI3RM8"}},{"cell_type":"markdown","source":">Before you start, let’s first import all the functions and classes you will use. This assumes a working SciPy environment with the Keras deep learning library installed.","metadata":{"id":"u8XrVDSL4fwe"}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout\nfrom tensorflow.keras.layers import LSTM\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error","metadata":{"id":"gb2FybxA4cNr","execution":{"iopub.status.busy":"2023-11-01T09:59:35.316100Z","iopub.execute_input":"2023-11-01T09:59:35.316457Z","iopub.status.idle":"2023-11-01T09:59:35.323208Z","shell.execute_reply.started":"2023-11-01T09:59:35.316427Z","shell.execute_reply":"2023-11-01T09:59:35.321365Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":">LSTMs are sensitive to the scale of the input data, specifically when the sigmoid (default) or tanh activation functions are used. It can be a good practice to rescale the data to the range of 0-to-1, also called normalizing. You can easily normalize the dataset using the MinMaxScaler preprocessing class from the scikit-learn library.","metadata":{"id":"LVW9qsY24xl3"}},{"cell_type":"code","source":"# normalize the dataset\nscaler = MinMaxScaler(feature_range=(0, 1))\ndataset = scaler.fit_transform(dataset)","metadata":{"id":"IVs0D8Fw49yG","execution":{"iopub.status.busy":"2023-11-01T09:59:35.324998Z","iopub.execute_input":"2023-11-01T09:59:35.325969Z","iopub.status.idle":"2023-11-01T09:59:35.345026Z","shell.execute_reply.started":"2023-11-01T09:59:35.325918Z","shell.execute_reply":"2023-11-01T09:59:35.343796Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":">After you model the data and estimate the skill of your model on the training dataset, you need to get an idea of the skill of the model on new unseen data. For a normal classification or regression problem, you would do this using cross validation.\n\n>With time series data, the sequence of values is important. A simple method that you can use is to split the ordered dataset into train and test datasets. The code below calculates the index of the split point and separates the data into the training datasets, with 67% of the observations used to train the model, leaving the remaining 33% for testing the model.","metadata":{"id":"u9J8Jyjx5G5g"}},{"cell_type":"code","source":"# split into train and test sets\ntrain_size = int(len(dataset) * 0.67)\ntest_size = len(dataset) - train_size\ntrain, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\nprint(len(train), len(test))","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XSz-Kzkn5JBi","outputId":"d9bc7409-e31c-4eaa-e08b-c6b598df5337","execution":{"iopub.status.busy":"2023-11-01T09:59:35.348219Z","iopub.execute_input":"2023-11-01T09:59:35.348914Z","iopub.status.idle":"2023-11-01T09:59:35.357977Z","shell.execute_reply.started":"2023-11-01T09:59:35.348850Z","shell.execute_reply":"2023-11-01T09:59:35.357119Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"97 48\n","output_type":"stream"}]},{"cell_type":"markdown","source":"> You can write a simple function to convert the single column of data into a two-column dataset: the first column containing this month’s (t) passenger count and the second column containing next month’s (t+1) passenger count to be predicted.\n\n>Now, you can define a function to create a new dataset, as described above. The function takes two arguments: the dataset, which is a NumPy array you want to convert into a dataset, and the look_back, which is the number of previous time steps to use as input variables to predict the next time period—in this case, defaulted to 1.\n\n>This default will create a dataset where X is the number of passengers at a given time (t), and Y is the number of passengers at the next time (t + 1).\n\nIt can be configured by constructing a differently shaped dataset in the next section.","metadata":{"id":"7xORoyyN5VDH"}},{"cell_type":"code","source":"# convert an array of values into a dataset matrix\ndef create_dataset(dataset, look_back=1):\n\tdataX, dataY = [], []\n\tfor i in range(len(dataset)-look_back-1):\n\t\ta = dataset[i:(i+look_back), 0]\n\t\tdataX.append(a)\n\t\tdataY.append(dataset[i + look_back, 0])\n\treturn np.array(dataX), np.array(dataY)","metadata":{"id":"tRU_Z7wn5SdY","execution":{"iopub.status.busy":"2023-11-01T09:59:35.359098Z","iopub.execute_input":"2023-11-01T09:59:35.359616Z","iopub.status.idle":"2023-11-01T09:59:35.371088Z","shell.execute_reply.started":"2023-11-01T09:59:35.359587Z","shell.execute_reply":"2023-11-01T09:59:35.369876Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"Let’s take a look at the effect of this function on the first rows of the dataset (shown in the unnormalized form for clarity).\n\n```\nX\t\tY\n112\t\t118\n118\t\t132\n132\t\t129\n129\t\t121\n121\t\t135\n```\n\nIf you compare these first five rows to the original dataset sample listed in the previous section, you can see the X=t and Y=t+1 pattern in the numbers.\n\nLet’s use this function to prepare the train and test datasets for modeling.","metadata":{"id":"Whxs1blW5zoe"}},{"cell_type":"code","source":"# reshape into X=t and Y=t+1\nlook_back = 1\ntrainX, trainY = create_dataset(train, look_back)\ntestX, testY = create_dataset(test, look_back)","metadata":{"id":"XFnHCApl6CMc","execution":{"iopub.status.busy":"2023-11-01T09:59:35.372869Z","iopub.execute_input":"2023-11-01T09:59:35.373320Z","iopub.status.idle":"2023-11-01T09:59:35.381175Z","shell.execute_reply.started":"2023-11-01T09:59:35.373280Z","shell.execute_reply":"2023-11-01T09:59:35.380173Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"The LSTM network expects the input data (X) to be provided with a specific array structure in the form of [samples, time steps, features].\n\nCurrently, the data is in the form of [samples, features], and you are framing the problem as one time step for each sample. You can transform the prepared train and test input data into the expected structure using numpy.reshape() as follows:","metadata":{"id":"fFo5NhgW6JNc"}},{"cell_type":"code","source":"# reshape input to be [samples, time steps, features]\ntrainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\ntestX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n\nprint(trainX.shape)\nprint(testX.shape)\n","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XAkPk_Hp6K3k","outputId":"764c5681-a6f7-432f-a5c6-0a0f4f557d18","execution":{"iopub.status.busy":"2023-11-01T09:59:35.382730Z","iopub.execute_input":"2023-11-01T09:59:35.383166Z","iopub.status.idle":"2023-11-01T09:59:35.395073Z","shell.execute_reply.started":"2023-11-01T09:59:35.383127Z","shell.execute_reply":"2023-11-01T09:59:35.393983Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"(95, 1, 1)\n(46, 1, 1)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Design and Evaluation of the LSTM network \n>You are now ready to design and fit your LSTM network for this problem.\n\n>The network has a visible layer with 1 input, a hidden layer with 4 LSTM blocks or neurons, and an output layer that makes a single value prediction. The default sigmoid activation function is used for the LSTM blocks. The network is trained for 100 epochs, and a batch size of 1 is used.","metadata":{"id":"LHC1cFCL6_mK"}},{"cell_type":"code","source":"# create and fit the LSTM network\nmodel = Sequential()\nmodel.add(LSTM(4, input_shape=(1, look_back)))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nmodel.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3PohE8fG7Nz8","outputId":"5bdae2c9-d435-470b-b40d-c62a9d6f1aab","execution":{"iopub.status.busy":"2023-11-01T09:59:35.397921Z","iopub.execute_input":"2023-11-01T09:59:35.398378Z","iopub.status.idle":"2023-11-01T09:59:54.925208Z","shell.execute_reply.started":"2023-11-01T09:59:35.398338Z","shell.execute_reply":"2023-11-01T09:59:54.924415Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Epoch 1/100\n95/95 - 2s - loss: 0.0535 - 2s/epoch - 24ms/step\nEpoch 2/100\n95/95 - 0s - loss: 0.0276 - 167ms/epoch - 2ms/step\nEpoch 3/100\n95/95 - 0s - loss: 0.0210 - 168ms/epoch - 2ms/step\nEpoch 4/100\n95/95 - 0s - loss: 0.0191 - 166ms/epoch - 2ms/step\nEpoch 5/100\n95/95 - 0s - loss: 0.0180 - 186ms/epoch - 2ms/step\nEpoch 6/100\n95/95 - 0s - loss: 0.0170 - 150ms/epoch - 2ms/step\nEpoch 7/100\n95/95 - 0s - loss: 0.0161 - 175ms/epoch - 2ms/step\nEpoch 8/100\n95/95 - 0s - loss: 0.0152 - 165ms/epoch - 2ms/step\nEpoch 9/100\n95/95 - 0s - loss: 0.0142 - 165ms/epoch - 2ms/step\nEpoch 10/100\n95/95 - 0s - loss: 0.0132 - 162ms/epoch - 2ms/step\nEpoch 11/100\n95/95 - 0s - loss: 0.0121 - 171ms/epoch - 2ms/step\nEpoch 12/100\n95/95 - 0s - loss: 0.0111 - 169ms/epoch - 2ms/step\nEpoch 13/100\n95/95 - 0s - loss: 0.0102 - 164ms/epoch - 2ms/step\nEpoch 14/100\n95/95 - 0s - loss: 0.0090 - 161ms/epoch - 2ms/step\nEpoch 15/100\n95/95 - 0s - loss: 0.0080 - 171ms/epoch - 2ms/step\nEpoch 16/100\n95/95 - 0s - loss: 0.0070 - 175ms/epoch - 2ms/step\nEpoch 17/100\n95/95 - 0s - loss: 0.0062 - 172ms/epoch - 2ms/step\nEpoch 18/100\n95/95 - 0s - loss: 0.0053 - 164ms/epoch - 2ms/step\nEpoch 19/100\n95/95 - 0s - loss: 0.0046 - 171ms/epoch - 2ms/step\nEpoch 20/100\n95/95 - 0s - loss: 0.0040 - 167ms/epoch - 2ms/step\nEpoch 21/100\n95/95 - 0s - loss: 0.0034 - 158ms/epoch - 2ms/step\nEpoch 22/100\n95/95 - 0s - loss: 0.0031 - 159ms/epoch - 2ms/step\nEpoch 23/100\n95/95 - 0s - loss: 0.0027 - 163ms/epoch - 2ms/step\nEpoch 24/100\n95/95 - 0s - loss: 0.0025 - 161ms/epoch - 2ms/step\nEpoch 25/100\n95/95 - 0s - loss: 0.0023 - 155ms/epoch - 2ms/step\nEpoch 26/100\n95/95 - 0s - loss: 0.0022 - 160ms/epoch - 2ms/step\nEpoch 27/100\n95/95 - 0s - loss: 0.0022 - 161ms/epoch - 2ms/step\nEpoch 28/100\n95/95 - 0s - loss: 0.0021 - 210ms/epoch - 2ms/step\nEpoch 29/100\n95/95 - 0s - loss: 0.0021 - 201ms/epoch - 2ms/step\nEpoch 30/100\n95/95 - 0s - loss: 0.0020 - 198ms/epoch - 2ms/step\nEpoch 31/100\n95/95 - 0s - loss: 0.0020 - 197ms/epoch - 2ms/step\nEpoch 32/100\n95/95 - 0s - loss: 0.0020 - 182ms/epoch - 2ms/step\nEpoch 33/100\n95/95 - 0s - loss: 0.0020 - 169ms/epoch - 2ms/step\nEpoch 34/100\n95/95 - 0s - loss: 0.0020 - 182ms/epoch - 2ms/step\nEpoch 35/100\n95/95 - 0s - loss: 0.0020 - 167ms/epoch - 2ms/step\nEpoch 36/100\n95/95 - 0s - loss: 0.0020 - 165ms/epoch - 2ms/step\nEpoch 37/100\n95/95 - 0s - loss: 0.0021 - 177ms/epoch - 2ms/step\nEpoch 38/100\n95/95 - 0s - loss: 0.0020 - 170ms/epoch - 2ms/step\nEpoch 39/100\n95/95 - 0s - loss: 0.0020 - 168ms/epoch - 2ms/step\nEpoch 40/100\n95/95 - 0s - loss: 0.0021 - 171ms/epoch - 2ms/step\nEpoch 41/100\n95/95 - 0s - loss: 0.0020 - 155ms/epoch - 2ms/step\nEpoch 42/100\n95/95 - 0s - loss: 0.0020 - 166ms/epoch - 2ms/step\nEpoch 43/100\n95/95 - 0s - loss: 0.0020 - 165ms/epoch - 2ms/step\nEpoch 44/100\n95/95 - 0s - loss: 0.0020 - 168ms/epoch - 2ms/step\nEpoch 45/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 46/100\n95/95 - 0s - loss: 0.0021 - 186ms/epoch - 2ms/step\nEpoch 47/100\n95/95 - 0s - loss: 0.0020 - 165ms/epoch - 2ms/step\nEpoch 48/100\n95/95 - 0s - loss: 0.0020 - 171ms/epoch - 2ms/step\nEpoch 49/100\n95/95 - 0s - loss: 0.0020 - 163ms/epoch - 2ms/step\nEpoch 50/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 51/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 52/100\n95/95 - 0s - loss: 0.0020 - 168ms/epoch - 2ms/step\nEpoch 53/100\n95/95 - 0s - loss: 0.0021 - 158ms/epoch - 2ms/step\nEpoch 54/100\n95/95 - 0s - loss: 0.0020 - 158ms/epoch - 2ms/step\nEpoch 55/100\n95/95 - 0s - loss: 0.0020 - 167ms/epoch - 2ms/step\nEpoch 56/100\n95/95 - 0s - loss: 0.0020 - 162ms/epoch - 2ms/step\nEpoch 57/100\n95/95 - 0s - loss: 0.0020 - 161ms/epoch - 2ms/step\nEpoch 58/100\n95/95 - 0s - loss: 0.0020 - 169ms/epoch - 2ms/step\nEpoch 59/100\n95/95 - 0s - loss: 0.0020 - 186ms/epoch - 2ms/step\nEpoch 60/100\n95/95 - 0s - loss: 0.0020 - 163ms/epoch - 2ms/step\nEpoch 61/100\n95/95 - 0s - loss: 0.0021 - 170ms/epoch - 2ms/step\nEpoch 62/100\n95/95 - 0s - loss: 0.0020 - 172ms/epoch - 2ms/step\nEpoch 63/100\n95/95 - 0s - loss: 0.0020 - 202ms/epoch - 2ms/step\nEpoch 64/100\n95/95 - 0s - loss: 0.0021 - 160ms/epoch - 2ms/step\nEpoch 65/100\n95/95 - 0s - loss: 0.0020 - 166ms/epoch - 2ms/step\nEpoch 66/100\n95/95 - 0s - loss: 0.0020 - 170ms/epoch - 2ms/step\nEpoch 67/100\n95/95 - 0s - loss: 0.0021 - 164ms/epoch - 2ms/step\nEpoch 68/100\n95/95 - 0s - loss: 0.0021 - 166ms/epoch - 2ms/step\nEpoch 69/100\n95/95 - 0s - loss: 0.0020 - 176ms/epoch - 2ms/step\nEpoch 70/100\n95/95 - 0s - loss: 0.0020 - 165ms/epoch - 2ms/step\nEpoch 71/100\n95/95 - 0s - loss: 0.0021 - 172ms/epoch - 2ms/step\nEpoch 72/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 73/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 74/100\n95/95 - 0s - loss: 0.0020 - 162ms/epoch - 2ms/step\nEpoch 75/100\n95/95 - 0s - loss: 0.0020 - 164ms/epoch - 2ms/step\nEpoch 76/100\n95/95 - 0s - loss: 0.0021 - 159ms/epoch - 2ms/step\nEpoch 77/100\n95/95 - 0s - loss: 0.0021 - 164ms/epoch - 2ms/step\nEpoch 78/100\n95/95 - 0s - loss: 0.0020 - 168ms/epoch - 2ms/step\nEpoch 79/100\n95/95 - 0s - loss: 0.0021 - 159ms/epoch - 2ms/step\nEpoch 80/100\n95/95 - 0s - loss: 0.0020 - 164ms/epoch - 2ms/step\nEpoch 81/100\n95/95 - 0s - loss: 0.0020 - 157ms/epoch - 2ms/step\nEpoch 82/100\n95/95 - 0s - loss: 0.0020 - 167ms/epoch - 2ms/step\nEpoch 83/100\n95/95 - 0s - loss: 0.0020 - 157ms/epoch - 2ms/step\nEpoch 84/100\n95/95 - 0s - loss: 0.0020 - 157ms/epoch - 2ms/step\nEpoch 85/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 86/100\n95/95 - 0s - loss: 0.0020 - 169ms/epoch - 2ms/step\nEpoch 87/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 88/100\n95/95 - 0s - loss: 0.0020 - 161ms/epoch - 2ms/step\nEpoch 89/100\n95/95 - 0s - loss: 0.0020 - 176ms/epoch - 2ms/step\nEpoch 90/100\n95/95 - 0s - loss: 0.0020 - 170ms/epoch - 2ms/step\nEpoch 91/100\n95/95 - 0s - loss: 0.0020 - 179ms/epoch - 2ms/step\nEpoch 92/100\n95/95 - 0s - loss: 0.0020 - 164ms/epoch - 2ms/step\nEpoch 93/100\n95/95 - 0s - loss: 0.0020 - 164ms/epoch - 2ms/step\nEpoch 94/100\n95/95 - 0s - loss: 0.0020 - 166ms/epoch - 2ms/step\nEpoch 95/100\n95/95 - 0s - loss: 0.0020 - 161ms/epoch - 2ms/step\nEpoch 96/100\n95/95 - 0s - loss: 0.0020 - 158ms/epoch - 2ms/step\nEpoch 97/100\n95/95 - 0s - loss: 0.0020 - 160ms/epoch - 2ms/step\nEpoch 98/100\n95/95 - 0s - loss: 0.0020 - 177ms/epoch - 2ms/step\nEpoch 99/100\n95/95 - 0s - loss: 0.0022 - 177ms/epoch - 2ms/step\nEpoch 100/100\n95/95 - 0s - loss: 0.0020 - 169ms/epoch - 2ms/step\n","output_type":"stream"},{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x7c889b8e95a0>"},"metadata":{}}]},{"cell_type":"markdown","source":"Once the model is fit, you can estimate the performance of the model on the train and test datasets. This will give you a point of comparison for new models.\n\nNote that you will invert the predictions before calculating error scores to ensure that performance is reported in the same units as the original data (thousands of passengers per month).","metadata":{"id":"rwGjfy6z7hzB"}},{"cell_type":"code","source":"# make predictions\ntrainPredict = model.predict(trainX)\ntestPredict = model.predict(testX)\n# invert predictions\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n# calculate root mean squared error\ntrainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w52cK3C47ixs","outputId":"1e3cd4ef-bd1a-4b71-d0a7-0b3abe3d8765","execution":{"iopub.status.busy":"2023-11-01T09:59:54.926291Z","iopub.execute_input":"2023-11-01T09:59:54.926837Z","iopub.status.idle":"2023-11-01T09:59:55.481285Z","shell.execute_reply.started":"2023-11-01T09:59:54.926807Z","shell.execute_reply":"2023-11-01T09:59:55.479948Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"3/3 [==============================] - 0s 3ms/step\n2/2 [==============================] - 0s 5ms/step\nTrain Score: 22.81 RMSE\nTest Score: 50.37 RMSE\n","output_type":"stream"}]},{"cell_type":"markdown","source":">You can see that the model has an average error of about 23 passengers (in thousands) on the training dataset and about 49 passengers (in thousands) on the test dataset. Not that bad.\n\n>Finally, you can generate predictions using the model for both the train and test dataset to get a visual indication of the skill of the model.\n\n>Because of how the dataset was prepared, you must shift the predictions so that they align on the x-axis with the original dataset. Once prepared, the data is plotted, showing the original dataset in blue, the predictions for the training dataset in green, and the predictions on the unseen test dataset in red","metadata":{"id":"DX2ba__n7p8w"}},{"cell_type":"code","source":"# shift train predictions for plotting\ntrainPredictPlot = np.empty_like(dataset)\ntrainPredictPlot[:, :] = np.nan\ntrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n# shift test predictions for plotting\ntestPredictPlot = np.empty_like(dataset)\ntestPredictPlot[:, :] = np.nan\ntestPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n# plot baseline and predictions\nplt.plot(scaler.inverse_transform(dataset))\nplt.plot(trainPredictPlot)\nplt.plot(testPredictPlot)\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"ejZA9Oyu7spv","outputId":"8b3d4335-4c8a-4992-9c65-6522f73f304f","execution":{"iopub.status.busy":"2023-11-01T09:59:55.485338Z","iopub.execute_input":"2023-11-01T09:59:55.485696Z","iopub.status.idle":"2023-11-01T09:59:55.682888Z","shell.execute_reply.started":"2023-11-01T09:59:55.485667Z","shell.execute_reply":"2023-11-01T09:59:55.681610Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":">You can see that the model did an excellent job of fitting both the training and the test datasets.","metadata":{"id":"E4TYIc5-72ZY"}},{"cell_type":"markdown","source":"# LSTM for Regression Using the Window Method\nYou can also phrase the problem so that multiple, recent time steps can be used to make the prediction for the next time step.\n\nThis is called a window, and the size of the window is a parameter that can be tuned for each problem.\n\nFor example, given the current time (t) to predict the value at the next time in the sequence (t+1), you can use the current time (t), as well as the two prior times (t-1 and t-2) as input variables.\n\nWhen phrased as a regression problem, the input variables are t-2, t-1, and t, and the output variable is t+1.\n\nThe create_dataset() function created in the previous section allows you to create this formulation of the time series problem by increasing the look_back argument from 1 to 3.\n\nA sample of the dataset with this formulation is as follows:\n\n\n```\nX1\tX2\tX3\tY\n112\t118\t132\t129\n118\t132\t129\t121\n132\t129\t121\t135\n129\t121\t135\t148\n121\t135\t148\t148\n```\nYou can re-run the example in the previous section with the larger window size. The whole code listing with just the window size change is listed below for completeness.\n","metadata":{"id":"AwYc1EDW8U9s"}},{"cell_type":"code","source":"# reshape into X=t and Y=t+1\nlook_back = 12\ntrainX, trainY = create_dataset(train, look_back)\ntestX, testY = create_dataset(test, look_back)\n# reshape input to be [samples, time steps, features]\ntrainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\ntestX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n\n# create and fit the LSTM network\nmodel = Sequential()\nmodel.add(LSTM(4, input_shape=(1, look_back),return_sequences=True))\nmodel.add(Dense(1))\nmodel.add(LSTM(4))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nmodel.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)\n\n# make predictions\ntrainPredict = model.predict(trainX)\ntestPredict = model.predict(testX)\n# invert predictions\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n# calculate root mean squared error\ntrainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))\n# shift train predictions for plotting\ntrainPredictPlot = np.empty_like(dataset)\ntrainPredictPlot[:, :] = np.nan\ntrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n# shift test predictions for plotting\ntestPredictPlot = np.empty_like(dataset)\ntestPredictPlot[:, :] = np.nan\ntestPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n# plot baseline and predictions\nplt.plot(scaler.inverse_transform(dataset))\nplt.plot(trainPredictPlot)\nplt.plot(testPredictPlot)\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"aTkqHEf77xRM","outputId":"ce69159c-7a40-4e33-a502-fd6f1b9f4144","execution":{"iopub.status.busy":"2023-11-01T09:59:55.684433Z","iopub.execute_input":"2023-11-01T09:59:55.684760Z","iopub.status.idle":"2023-11-01T10:00:26.562463Z","shell.execute_reply.started":"2023-11-01T09:59:55.684732Z","shell.execute_reply":"2023-11-01T10:00:26.561251Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"Epoch 1/100\n84/84 - 4s - loss: 0.0631 - 4s/epoch - 42ms/step\nEpoch 2/100\n84/84 - 0s - loss: 0.0371 - 222ms/epoch - 3ms/step\nEpoch 3/100\n84/84 - 0s - loss: 0.0156 - 221ms/epoch - 3ms/step\nEpoch 4/100\n84/84 - 0s - loss: 0.0094 - 215ms/epoch - 3ms/step\nEpoch 5/100\n84/84 - 0s - loss: 0.0075 - 241ms/epoch - 3ms/step\nEpoch 6/100\n84/84 - 0s - loss: 0.0065 - 245ms/epoch - 3ms/step\nEpoch 7/100\n84/84 - 0s - loss: 0.0058 - 232ms/epoch - 3ms/step\nEpoch 8/100\n84/84 - 0s - loss: 0.0053 - 230ms/epoch - 3ms/step\nEpoch 9/100\n84/84 - 0s - loss: 0.0049 - 260ms/epoch - 3ms/step\nEpoch 10/100\n84/84 - 0s - loss: 0.0044 - 252ms/epoch - 3ms/step\nEpoch 11/100\n84/84 - 0s - loss: 0.0041 - 234ms/epoch - 3ms/step\nEpoch 12/100\n84/84 - 0s - loss: 0.0038 - 230ms/epoch - 3ms/step\nEpoch 13/100\n84/84 - 0s - loss: 0.0035 - 226ms/epoch - 3ms/step\nEpoch 14/100\n84/84 - 0s - loss: 0.0033 - 249ms/epoch - 3ms/step\nEpoch 15/100\n84/84 - 0s - loss: 0.0031 - 257ms/epoch - 3ms/step\nEpoch 16/100\n84/84 - 0s - loss: 0.0029 - 258ms/epoch - 3ms/step\nEpoch 17/100\n84/84 - 0s - loss: 0.0028 - 276ms/epoch - 3ms/step\nEpoch 18/100\n84/84 - 0s - loss: 0.0026 - 262ms/epoch - 3ms/step\nEpoch 19/100\n84/84 - 0s - loss: 0.0025 - 248ms/epoch - 3ms/step\nEpoch 20/100\n84/84 - 0s - loss: 0.0024 - 238ms/epoch - 3ms/step\nEpoch 21/100\n84/84 - 0s - loss: 0.0022 - 234ms/epoch - 3ms/step\nEpoch 22/100\n84/84 - 0s - loss: 0.0021 - 229ms/epoch - 3ms/step\nEpoch 23/100\n84/84 - 0s - loss: 0.0020 - 223ms/epoch - 3ms/step\nEpoch 24/100\n84/84 - 0s - loss: 0.0019 - 255ms/epoch - 3ms/step\nEpoch 25/100\n84/84 - 0s - loss: 0.0018 - 256ms/epoch - 3ms/step\nEpoch 26/100\n84/84 - 0s - loss: 0.0017 - 260ms/epoch - 3ms/step\nEpoch 27/100\n84/84 - 0s - loss: 0.0017 - 249ms/epoch - 3ms/step\nEpoch 28/100\n84/84 - 0s - loss: 0.0016 - 255ms/epoch - 3ms/step\nEpoch 29/100\n84/84 - 0s - loss: 0.0014 - 242ms/epoch - 3ms/step\nEpoch 30/100\n84/84 - 0s - loss: 0.0014 - 237ms/epoch - 3ms/step\nEpoch 31/100\n84/84 - 0s - loss: 0.0013 - 245ms/epoch - 3ms/step\nEpoch 32/100\n84/84 - 0s - loss: 0.0012 - 256ms/epoch - 3ms/step\nEpoch 33/100\n84/84 - 0s - loss: 0.0012 - 263ms/epoch - 3ms/step\nEpoch 34/100\n84/84 - 0s - loss: 0.0011 - 262ms/epoch - 3ms/step\nEpoch 35/100\n84/84 - 0s - loss: 0.0011 - 258ms/epoch - 3ms/step\nEpoch 36/100\n84/84 - 0s - loss: 0.0012 - 250ms/epoch - 3ms/step\nEpoch 37/100\n84/84 - 0s - loss: 0.0011 - 244ms/epoch - 3ms/step\nEpoch 38/100\n84/84 - 0s - loss: 0.0010 - 247ms/epoch - 3ms/step\nEpoch 39/100\n84/84 - 0s - loss: 9.7136e-04 - 274ms/epoch - 3ms/step\nEpoch 40/100\n84/84 - 0s - loss: 9.7746e-04 - 307ms/epoch - 4ms/step\nEpoch 41/100\n84/84 - 0s - loss: 9.2030e-04 - 279ms/epoch - 3ms/step\nEpoch 42/100\n84/84 - 0s - loss: 9.3241e-04 - 284ms/epoch - 3ms/step\nEpoch 43/100\n84/84 - 0s - loss: 9.8913e-04 - 296ms/epoch - 4ms/step\nEpoch 44/100\n84/84 - 0s - loss: 9.2080e-04 - 278ms/epoch - 3ms/step\nEpoch 45/100\n84/84 - 0s - loss: 8.9766e-04 - 251ms/epoch - 3ms/step\nEpoch 46/100\n84/84 - 0s - loss: 9.0627e-04 - 262ms/epoch - 3ms/step\nEpoch 47/100\n84/84 - 0s - loss: 9.2318e-04 - 243ms/epoch - 3ms/step\nEpoch 48/100\n84/84 - 0s - loss: 8.8331e-04 - 260ms/epoch - 3ms/step\nEpoch 49/100\n84/84 - 0s - loss: 8.6243e-04 - 238ms/epoch - 3ms/step\nEpoch 50/100\n84/84 - 0s - loss: 8.6017e-04 - 276ms/epoch - 3ms/step\nEpoch 51/100\n84/84 - 0s - loss: 8.4666e-04 - 294ms/epoch - 4ms/step\nEpoch 52/100\n84/84 - 0s - loss: 8.5013e-04 - 281ms/epoch - 3ms/step\nEpoch 53/100\n84/84 - 0s - loss: 8.2507e-04 - 277ms/epoch - 3ms/step\nEpoch 54/100\n84/84 - 0s - loss: 9.0284e-04 - 312ms/epoch - 4ms/step\nEpoch 55/100\n84/84 - 0s - loss: 8.2433e-04 - 300ms/epoch - 4ms/step\nEpoch 56/100\n84/84 - 0s - loss: 8.0770e-04 - 335ms/epoch - 4ms/step\nEpoch 57/100\n84/84 - 0s - loss: 8.7611e-04 - 298ms/epoch - 4ms/step\nEpoch 58/100\n84/84 - 0s - loss: 8.1952e-04 - 272ms/epoch - 3ms/step\nEpoch 59/100\n84/84 - 0s - loss: 8.3723e-04 - 271ms/epoch - 3ms/step\nEpoch 60/100\n84/84 - 0s - loss: 7.9744e-04 - 275ms/epoch - 3ms/step\nEpoch 61/100\n84/84 - 0s - loss: 7.8987e-04 - 242ms/epoch - 3ms/step\nEpoch 62/100\n84/84 - 0s - loss: 7.7284e-04 - 246ms/epoch - 3ms/step\nEpoch 63/100\n84/84 - 0s - loss: 7.6788e-04 - 272ms/epoch - 3ms/step\nEpoch 64/100\n84/84 - 0s - loss: 7.5887e-04 - 251ms/epoch - 3ms/step\nEpoch 65/100\n84/84 - 0s - loss: 7.4543e-04 - 242ms/epoch - 3ms/step\nEpoch 66/100\n84/84 - 0s - loss: 7.8528e-04 - 236ms/epoch - 3ms/step\nEpoch 67/100\n84/84 - 0s - loss: 7.8832e-04 - 234ms/epoch - 3ms/step\nEpoch 68/100\n84/84 - 0s - loss: 7.7145e-04 - 245ms/epoch - 3ms/step\nEpoch 69/100\n84/84 - 0s - loss: 8.1982e-04 - 258ms/epoch - 3ms/step\nEpoch 70/100\n84/84 - 0s - loss: 7.2319e-04 - 259ms/epoch - 3ms/step\nEpoch 71/100\n84/84 - 0s - loss: 7.6790e-04 - 259ms/epoch - 3ms/step\nEpoch 72/100\n84/84 - 0s - loss: 7.2551e-04 - 264ms/epoch - 3ms/step\nEpoch 73/100\n84/84 - 0s - loss: 7.2941e-04 - 287ms/epoch - 3ms/step\nEpoch 74/100\n84/84 - 0s - loss: 7.3201e-04 - 254ms/epoch - 3ms/step\nEpoch 75/100\n84/84 - 0s - loss: 7.4028e-04 - 247ms/epoch - 3ms/step\nEpoch 76/100\n84/84 - 0s - loss: 7.0270e-04 - 246ms/epoch - 3ms/step\nEpoch 77/100\n84/84 - 0s - loss: 7.2972e-04 - 268ms/epoch - 3ms/step\nEpoch 78/100\n84/84 - 0s - loss: 7.3249e-04 - 270ms/epoch - 3ms/step\nEpoch 79/100\n84/84 - 0s - loss: 6.9040e-04 - 276ms/epoch - 3ms/step\nEpoch 80/100\n84/84 - 0s - loss: 7.3008e-04 - 248ms/epoch - 3ms/step\nEpoch 81/100\n84/84 - 0s - loss: 7.3700e-04 - 246ms/epoch - 3ms/step\nEpoch 82/100\n84/84 - 0s - loss: 7.2125e-04 - 257ms/epoch - 3ms/step\nEpoch 83/100\n84/84 - 0s - loss: 7.0834e-04 - 260ms/epoch - 3ms/step\nEpoch 84/100\n84/84 - 0s - loss: 7.0752e-04 - 240ms/epoch - 3ms/step\nEpoch 85/100\n84/84 - 0s - loss: 7.0624e-04 - 238ms/epoch - 3ms/step\nEpoch 86/100\n84/84 - 0s - loss: 7.1736e-04 - 259ms/epoch - 3ms/step\nEpoch 87/100\n84/84 - 0s - loss: 7.0402e-04 - 227ms/epoch - 3ms/step\nEpoch 88/100\n84/84 - 0s - loss: 7.0521e-04 - 252ms/epoch - 3ms/step\nEpoch 89/100\n84/84 - 0s - loss: 6.8773e-04 - 245ms/epoch - 3ms/step\nEpoch 90/100\n84/84 - 0s - loss: 7.0847e-04 - 259ms/epoch - 3ms/step\nEpoch 91/100\n84/84 - 0s - loss: 7.2012e-04 - 259ms/epoch - 3ms/step\nEpoch 92/100\n84/84 - 0s - loss: 6.9206e-04 - 266ms/epoch - 3ms/step\nEpoch 93/100\n84/84 - 0s - loss: 6.7422e-04 - 239ms/epoch - 3ms/step\nEpoch 94/100\n84/84 - 0s - loss: 6.9074e-04 - 248ms/epoch - 3ms/step\nEpoch 95/100\n84/84 - 0s - loss: 6.8124e-04 - 250ms/epoch - 3ms/step\nEpoch 96/100\n84/84 - 0s - loss: 6.9991e-04 - 258ms/epoch - 3ms/step\nEpoch 97/100\n84/84 - 0s - loss: 6.8163e-04 - 240ms/epoch - 3ms/step\nEpoch 98/100\n84/84 - 0s - loss: 6.7262e-04 - 254ms/epoch - 3ms/step\nEpoch 99/100\n84/84 - 0s - loss: 6.8251e-04 - 245ms/epoch - 3ms/step\nEpoch 100/100\n84/84 - 0s - loss: 6.6786e-04 - 242ms/epoch - 3ms/step\n3/3 [==============================] - 1s 4ms/step\n2/2 [==============================] - 0s 5ms/step\nTrain Score: 13.56 RMSE\nTest Score: 61.62 RMSE\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[23], line 33\u001b[0m\n\u001b[1;32m     31\u001b[0m trainPredictPlot \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty_like(dataset)\n\u001b[1;32m     32\u001b[0m trainPredictPlot[:, :] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan\n\u001b[0;32m---> 33\u001b[0m \u001b[43mtrainPredictPlot\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlook_back\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrainPredict\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mlook_back\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m trainPredict\n\u001b[1;32m     34\u001b[0m \u001b[38;5;66;03m# shift test predictions for plotting\u001b[39;00m\n\u001b[1;32m     35\u001b[0m testPredictPlot \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty_like(dataset)\n","\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (84,4) into shape (84,1)"],"ename":"ValueError","evalue":"could not broadcast input array from shape (84,4) into shape (84,1)","output_type":"error"}]},{"cell_type":"markdown","source":"# LSTM for Regression with Time Steps\n>You may have noticed that the data preparation for the LSTM network includes time steps.\n\n>Some sequence problems may have a varied number of time steps per sample. For example, you may have measurements of a physical machine leading up to the point of failure or a point of surge. Each incident would be a sample of observations that lead up to the event, which would be the time steps, and the variables observed would be the features.\n\n>Time steps provide another way to phrase your time series problem. Like above in the window example, you can take prior time steps in your time series as inputs to predict the output at the next time step.\n\n>Instead of phrasing the past observations as separate input features, you can use them as time steps of the one input feature, which is indeed a more accurate framing of the problem.\n\n>You can do this using the same data representation as in the previous window-based example, except when you reshape the data, you set the columns to be the time steps dimension and change the features dimension back to 1. For example:\n\n\n\n```\n# reshape input to be [samples, time steps, features]\ntrainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\ntestX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))\n```\n\n","metadata":{"id":"3NyP3P6Z9Wko"}},{"cell_type":"code","source":"# reshape into X=t and Y=t+1\nlook_back = 3\ntrainX, trainY = create_dataset(train, look_back)\ntestX, testY = create_dataset(test, look_back)\n# reshape input to be [samples, time steps, features]\ntrainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\ntestX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))\n\nprint(trainX.shape)\nprint(trainY.shape)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rwz_a87O9hWt","outputId":"90dba386-27de-430f-883a-06c47663fdbc","execution":{"iopub.status.busy":"2023-11-01T10:01:53.148279Z","iopub.execute_input":"2023-11-01T10:01:53.148736Z","iopub.status.idle":"2023-11-01T10:01:53.158509Z","shell.execute_reply.started":"2023-11-01T10:01:53.148703Z","shell.execute_reply":"2023-11-01T10:01:53.157306Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"(93, 3, 1)\n(93,)\n","output_type":"stream"}]},{"cell_type":"code","source":"# create and fit the LSTM network\nmodel = Sequential()\nmodel.add(LSTM(4, input_shape=(look_back, 1)))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nmodel.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)\n# make predictions\ntrainPredict = model.predict(trainX)\ntestPredict = model.predict(testX)\n# invert predictions\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n# calculate root mean squared error\ntrainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))\n# shift train predictions for plotting\ntrainPredictPlot = np.empty_like(dataset)\ntrainPredictPlot[:, :] = np.nan\ntrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n# shift test predictions for plotting\ntestPredictPlot = np.empty_like(dataset)\ntestPredictPlot[:, :] = np.nan\ntestPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n# plot baseline and predictions\nplt.plot(scaler.inverse_transform(dataset))\nplt.plot(trainPredictPlot)\nplt.plot(testPredictPlot)\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"JEMfK1yUAYHA","outputId":"cacea859-99b0-4db9-f82f-37cb89db7ab8","execution":{"iopub.status.busy":"2023-11-01T10:01:53.160801Z","iopub.execute_input":"2023-11-01T10:01:53.161745Z","iopub.status.idle":"2023-11-01T10:02:19.274959Z","shell.execute_reply.started":"2023-11-01T10:01:53.161705Z","shell.execute_reply":"2023-11-01T10:02:19.273867Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Epoch 1/100\n93/93 - 2s - loss: 0.0339 - 2s/epoch - 24ms/step\nEpoch 2/100\n93/93 - 0s - loss: 0.0133 - 245ms/epoch - 3ms/step\nEpoch 3/100\n93/93 - 0s - loss: 0.0108 - 257ms/epoch - 3ms/step\nEpoch 4/100\n93/93 - 0s - loss: 0.0089 - 227ms/epoch - 2ms/step\nEpoch 5/100\n93/93 - 0s - loss: 0.0071 - 248ms/epoch - 3ms/step\nEpoch 6/100\n93/93 - 0s - loss: 0.0061 - 243ms/epoch - 3ms/step\nEpoch 7/100\n93/93 - 0s - loss: 0.0051 - 252ms/epoch - 3ms/step\nEpoch 8/100\n93/93 - 0s - loss: 0.0046 - 235ms/epoch - 3ms/step\nEpoch 9/100\n93/93 - 0s - loss: 0.0042 - 238ms/epoch - 3ms/step\nEpoch 10/100\n93/93 - 0s - loss: 0.0042 - 237ms/epoch - 3ms/step\nEpoch 11/100\n93/93 - 0s - loss: 0.0040 - 230ms/epoch - 2ms/step\nEpoch 12/100\n93/93 - 0s - loss: 0.0040 - 239ms/epoch - 3ms/step\nEpoch 13/100\n93/93 - 0s - loss: 0.0040 - 260ms/epoch - 3ms/step\nEpoch 14/100\n93/93 - 0s - loss: 0.0038 - 211ms/epoch - 2ms/step\nEpoch 15/100\n93/93 - 0s - loss: 0.0037 - 214ms/epoch - 2ms/step\nEpoch 16/100\n93/93 - 0s - loss: 0.0040 - 219ms/epoch - 2ms/step\nEpoch 17/100\n93/93 - 0s - loss: 0.0039 - 214ms/epoch - 2ms/step\nEpoch 18/100\n93/93 - 0s - loss: 0.0039 - 234ms/epoch - 3ms/step\nEpoch 19/100\n93/93 - 0s - loss: 0.0038 - 229ms/epoch - 2ms/step\nEpoch 20/100\n93/93 - 0s - loss: 0.0037 - 273ms/epoch - 3ms/step\nEpoch 21/100\n93/93 - 0s - loss: 0.0039 - 251ms/epoch - 3ms/step\nEpoch 22/100\n93/93 - 0s - loss: 0.0039 - 245ms/epoch - 3ms/step\nEpoch 23/100\n93/93 - 0s - loss: 0.0037 - 244ms/epoch - 3ms/step\nEpoch 24/100\n93/93 - 0s - loss: 0.0037 - 260ms/epoch - 3ms/step\nEpoch 25/100\n93/93 - 0s - loss: 0.0037 - 241ms/epoch - 3ms/step\nEpoch 26/100\n93/93 - 0s - loss: 0.0036 - 237ms/epoch - 3ms/step\nEpoch 27/100\n93/93 - 0s - loss: 0.0034 - 249ms/epoch - 3ms/step\nEpoch 28/100\n93/93 - 0s - loss: 0.0037 - 237ms/epoch - 3ms/step\nEpoch 29/100\n93/93 - 0s - loss: 0.0036 - 251ms/epoch - 3ms/step\nEpoch 30/100\n93/93 - 0s - loss: 0.0035 - 220ms/epoch - 2ms/step\nEpoch 31/100\n93/93 - 0s - loss: 0.0036 - 218ms/epoch - 2ms/step\nEpoch 32/100\n93/93 - 0s - loss: 0.0035 - 232ms/epoch - 2ms/step\nEpoch 33/100\n93/93 - 0s - loss: 0.0035 - 229ms/epoch - 2ms/step\nEpoch 34/100\n93/93 - 0s - loss: 0.0035 - 260ms/epoch - 3ms/step\nEpoch 35/100\n93/93 - 0s - loss: 0.0036 - 268ms/epoch - 3ms/step\nEpoch 36/100\n93/93 - 0s - loss: 0.0037 - 232ms/epoch - 2ms/step\nEpoch 37/100\n93/93 - 0s - loss: 0.0037 - 233ms/epoch - 3ms/step\nEpoch 38/100\n93/93 - 0s - loss: 0.0035 - 246ms/epoch - 3ms/step\nEpoch 39/100\n93/93 - 0s - loss: 0.0036 - 245ms/epoch - 3ms/step\nEpoch 40/100\n93/93 - 0s - loss: 0.0034 - 240ms/epoch - 3ms/step\nEpoch 41/100\n93/93 - 0s - loss: 0.0034 - 236ms/epoch - 3ms/step\nEpoch 42/100\n93/93 - 0s - loss: 0.0034 - 241ms/epoch - 3ms/step\nEpoch 43/100\n93/93 - 0s - loss: 0.0037 - 229ms/epoch - 2ms/step\nEpoch 44/100\n93/93 - 0s - loss: 0.0033 - 225ms/epoch - 2ms/step\nEpoch 45/100\n93/93 - 0s - loss: 0.0033 - 234ms/epoch - 3ms/step\nEpoch 46/100\n93/93 - 0s - loss: 0.0034 - 224ms/epoch - 2ms/step\nEpoch 47/100\n93/93 - 0s - loss: 0.0032 - 229ms/epoch - 2ms/step\nEpoch 48/100\n93/93 - 0s - loss: 0.0034 - 230ms/epoch - 2ms/step\nEpoch 49/100\n93/93 - 0s - loss: 0.0033 - 201ms/epoch - 2ms/step\nEpoch 50/100\n93/93 - 0s - loss: 0.0033 - 215ms/epoch - 2ms/step\nEpoch 51/100\n93/93 - 0s - loss: 0.0033 - 225ms/epoch - 2ms/step\nEpoch 52/100\n93/93 - 0s - loss: 0.0032 - 242ms/epoch - 3ms/step\nEpoch 53/100\n93/93 - 0s - loss: 0.0032 - 217ms/epoch - 2ms/step\nEpoch 54/100\n93/93 - 0s - loss: 0.0033 - 247ms/epoch - 3ms/step\nEpoch 55/100\n93/93 - 0s - loss: 0.0031 - 259ms/epoch - 3ms/step\nEpoch 56/100\n93/93 - 0s - loss: 0.0032 - 236ms/epoch - 3ms/step\nEpoch 57/100\n93/93 - 0s - loss: 0.0033 - 251ms/epoch - 3ms/step\nEpoch 58/100\n93/93 - 0s - loss: 0.0032 - 222ms/epoch - 2ms/step\nEpoch 59/100\n93/93 - 0s - loss: 0.0032 - 211ms/epoch - 2ms/step\nEpoch 60/100\n93/93 - 0s - loss: 0.0031 - 216ms/epoch - 2ms/step\nEpoch 61/100\n93/93 - 0s - loss: 0.0033 - 209ms/epoch - 2ms/step\nEpoch 62/100\n93/93 - 0s - loss: 0.0033 - 215ms/epoch - 2ms/step\nEpoch 63/100\n93/93 - 0s - loss: 0.0033 - 206ms/epoch - 2ms/step\nEpoch 64/100\n93/93 - 0s - loss: 0.0031 - 202ms/epoch - 2ms/step\nEpoch 65/100\n93/93 - 0s - loss: 0.0030 - 203ms/epoch - 2ms/step\nEpoch 66/100\n93/93 - 0s - loss: 0.0031 - 199ms/epoch - 2ms/step\nEpoch 67/100\n93/93 - 0s - loss: 0.0031 - 212ms/epoch - 2ms/step\nEpoch 68/100\n93/93 - 0s - loss: 0.0029 - 216ms/epoch - 2ms/step\nEpoch 69/100\n93/93 - 0s - loss: 0.0031 - 216ms/epoch - 2ms/step\nEpoch 70/100\n93/93 - 0s - loss: 0.0030 - 208ms/epoch - 2ms/step\nEpoch 71/100\n93/93 - 0s - loss: 0.0028 - 211ms/epoch - 2ms/step\nEpoch 72/100\n93/93 - 0s - loss: 0.0030 - 216ms/epoch - 2ms/step\nEpoch 73/100\n93/93 - 0s - loss: 0.0030 - 209ms/epoch - 2ms/step\nEpoch 74/100\n93/93 - 0s - loss: 0.0029 - 219ms/epoch - 2ms/step\nEpoch 75/100\n93/93 - 0s - loss: 0.0029 - 218ms/epoch - 2ms/step\nEpoch 76/100\n93/93 - 0s - loss: 0.0028 - 206ms/epoch - 2ms/step\nEpoch 77/100\n93/93 - 0s - loss: 0.0029 - 196ms/epoch - 2ms/step\nEpoch 78/100\n93/93 - 0s - loss: 0.0028 - 209ms/epoch - 2ms/step\nEpoch 79/100\n93/93 - 0s - loss: 0.0029 - 203ms/epoch - 2ms/step\nEpoch 80/100\n93/93 - 0s - loss: 0.0028 - 195ms/epoch - 2ms/step\nEpoch 81/100\n93/93 - 0s - loss: 0.0028 - 194ms/epoch - 2ms/step\nEpoch 82/100\n93/93 - 0s - loss: 0.0028 - 201ms/epoch - 2ms/step\nEpoch 83/100\n93/93 - 0s - loss: 0.0029 - 197ms/epoch - 2ms/step\nEpoch 84/100\n93/93 - 0s - loss: 0.0029 - 197ms/epoch - 2ms/step\nEpoch 85/100\n93/93 - 0s - loss: 0.0028 - 208ms/epoch - 2ms/step\nEpoch 86/100\n93/93 - 0s - loss: 0.0027 - 216ms/epoch - 2ms/step\nEpoch 87/100\n93/93 - 0s - loss: 0.0028 - 233ms/epoch - 3ms/step\nEpoch 88/100\n93/93 - 0s - loss: 0.0028 - 230ms/epoch - 2ms/step\nEpoch 89/100\n93/93 - 0s - loss: 0.0027 - 228ms/epoch - 2ms/step\nEpoch 90/100\n93/93 - 0s - loss: 0.0027 - 215ms/epoch - 2ms/step\nEpoch 91/100\n93/93 - 0s - loss: 0.0027 - 214ms/epoch - 2ms/step\nEpoch 92/100\n93/93 - 0s - loss: 0.0027 - 215ms/epoch - 2ms/step\nEpoch 93/100\n93/93 - 0s - loss: 0.0026 - 210ms/epoch - 2ms/step\nEpoch 94/100\n93/93 - 0s - loss: 0.0026 - 212ms/epoch - 2ms/step\nEpoch 95/100\n93/93 - 0s - loss: 0.0027 - 211ms/epoch - 2ms/step\nEpoch 96/100\n93/93 - 0s - loss: 0.0026 - 210ms/epoch - 2ms/step\nEpoch 97/100\n93/93 - 0s - loss: 0.0026 - 214ms/epoch - 2ms/step\nEpoch 98/100\n93/93 - 0s - loss: 0.0026 - 200ms/epoch - 2ms/step\nEpoch 99/100\n93/93 - 0s - loss: 0.0025 - 203ms/epoch - 2ms/step\nEpoch 100/100\n93/93 - 0s - loss: 0.0025 - 215ms/epoch - 2ms/step\n3/3 [==============================] - 0s 4ms/step\n2/2 [==============================] - 0s 4ms/step\nTrain Score: 25.78 RMSE\nTest Score: 61.15 RMSE\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"markdown","source":"# LSTM with Memory Between Batches\n>The LSTM network has memory capable of remembering across long sequences.\n\n>Normally, the state within the network is reset after each training batch when fitting the model, as well as each call to model.predict() or model.evaluate().\n\n>You can gain finer control over when the internal state of the LSTM network is cleared in Keras by making the LSTM layer “stateful.” This means it can build a state over the entire training sequence and even maintain that state if needed to make predictions.\n\n>It requires that the training data not be shuffled when fitting the network. It also requires explicit resetting of the network state after each exposure to the training data (epoch) by calls to model.reset_states(). This means that you must create your own outer loop of epochs and within each epoch call model.fit() and model.reset_states(). For example:\n\n```\nfor i in range(100):\n\tmodel.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)\n\tmodel.reset_states()\n```\n\nFinally, when the LSTM layer is constructed, the stateful parameter must be set to True. Instead of specifying the input dimensions, you must hard code the number of samples in a batch, the number of time steps in a sample, and the number of features in a time step by setting the batch_input_shape parameter. For example:\n\n\n`model.add(LSTM(4, batch_input_shape=(batch_size, time_steps, features), stateful=True))`\n\nThis same batch size must then be used later when evaluating the model and making predictions. For example:\n\n`model.predict(trainX, batch_size=batch_size)`\n\n","metadata":{"id":"bMgOCG4tIH8M"}},{"cell_type":"code","source":"# reshape into X=t and Y=t+1\nlook_back = 12\ntrainX, trainY = create_dataset(train, look_back)\ntestX, testY = create_dataset(test, look_back)\n# reshape input to be [samples, time steps, features]\ntrainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\ntestX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))\n# create and fit the LSTM network\nbatch_size = 1\nmodel = Sequential()\nmodel.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nfor i in range(100):\n\tmodel.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)\n\tmodel.reset_states()\n# make predictions\ntrainPredict = model.predict(trainX, batch_size=batch_size)\nmodel.reset_states()\ntestPredict = model.predict(testX, batch_size=batch_size)\nmodel.reset_states()\n# invert predictions\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n# calculate root mean squared error\ntrainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))\n# shift train predictions for plotting\ntrainPredictPlot = np.empty_like(dataset)\ntrainPredictPlot[:, :] = np.nan\ntrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n# shift test predictions for plotting\ntestPredictPlot = np.empty_like(dataset)\ntestPredictPlot[:, :] = np.nan\ntestPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n# plot baseline and predictions\nplt.plot(scaler.inverse_transform(dataset))\nplt.plot(trainPredictPlot)\nplt.plot(testPredictPlot)\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"EgFD6YvZJuvQ","outputId":"b8e9c0db-6e78-4aca-e07c-30d400d57498","execution":{"iopub.status.busy":"2023-11-01T10:02:19.276459Z","iopub.execute_input":"2023-11-01T10:02:19.277599Z","iopub.status.idle":"2023-11-01T10:03:13.502072Z","shell.execute_reply.started":"2023-11-01T10:02:19.277540Z","shell.execute_reply":"2023-11-01T10:03:13.500661Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"84/84 - 2s - loss: 0.0082 - 2s/epoch - 26ms/step\n84/84 - 0s - loss: 0.0123 - 334ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0075 - 311ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0055 - 318ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0046 - 316ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0041 - 310ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0039 - 341ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0038 - 347ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0038 - 357ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0037 - 315ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0037 - 310ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0036 - 304ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0036 - 321ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0036 - 339ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0036 - 382ms/epoch - 5ms/step\n84/84 - 0s - loss: 0.0035 - 354ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0035 - 355ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0035 - 350ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0035 - 314ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0034 - 331ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0034 - 335ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0034 - 345ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0034 - 356ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0034 - 359ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0034 - 319ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0033 - 304ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0033 - 324ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0033 - 336ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0033 - 324ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0033 - 348ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0032 - 337ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0032 - 351ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0032 - 311ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0032 - 357ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0032 - 317ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0031 - 343ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0031 - 310ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0031 - 309ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0031 - 322ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0030 - 311ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0030 - 307ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0030 - 307ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0030 - 292ms/epoch - 3ms/step\n84/84 - 0s - loss: 0.0029 - 296ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0029 - 320ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0029 - 330ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0029 - 325ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0028 - 313ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0028 - 337ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0028 - 331ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0027 - 314ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0027 - 301ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0027 - 327ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0027 - 324ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0026 - 318ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0026 - 348ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0026 - 325ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0026 - 297ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0025 - 316ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0025 - 361ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0025 - 372ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0025 - 293ms/epoch - 3ms/step\n84/84 - 0s - loss: 0.0024 - 304ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0024 - 330ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0024 - 337ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0024 - 324ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0024 - 316ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0023 - 289ms/epoch - 3ms/step\n84/84 - 0s - loss: 0.0023 - 321ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0023 - 308ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0023 - 296ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0023 - 303ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0023 - 319ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 329ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 352ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 308ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 324ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 332ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 298ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 317ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0022 - 340ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 370ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 307ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 311ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 326ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 329ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 335ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 317ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0021 - 344ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 344ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 301ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 323ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 336ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 338ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 359ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 304ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 319ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 304ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 322ms/epoch - 4ms/step\n84/84 - 0s - loss: 0.0020 - 310ms/epoch - 4ms/step\n84/84 [==============================] - 1s 2ms/step\n35/35 [==============================] - 0s 2ms/step\nTrain Score: 23.61 RMSE\nTest Score: 53.70 RMSE\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"markdown","source":"You do see that results are better than some, worse than others. The model may need more modules and may need to be trained for more epochs to internalize the structure of the problem.","metadata":{"id":"tdXIYsqbLhZ1"}},{"cell_type":"markdown","source":"# Stacked LSTMs with Memory Between Batches\nFinally, let’s take a look at one of the big benefits of LSTMs: the fact that they can be successfully trained when stacked into deep network architectures.\n\nLSTM networks can be stacked in Keras in the same way that other layer types can be stacked. One addition to the configuration that is required is that an LSTM layer prior to each subsequent LSTM layer must return the sequence. This can be done by setting the return_sequences parameter on the layer to True.\n\nYou can extend the stateful LSTM in the previous section to have two layers, as follows:\n\n```\nmodel.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True, return_sequences=True))\nmodel.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))\n\n```\n\n","metadata":{"id":"eexs34jUNPvk"}},{"cell_type":"code","source":"# reshape into X=t and Y=t+1\nlook_back = 12\ntrainX, trainY = create_dataset(train, look_back)\ntestX, testY = create_dataset(test, look_back)\n# reshape input to be [samples, time steps, features]\ntrainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\ntestX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))\n# create and fit the LSTM network\nbatch_size = 1\nmodel = Sequential()\nmodel.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True, return_sequences=True))\nmodel.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nfor i in range(300):\n\tmodel.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)\n\tmodel.reset_states()\n# make predictions\ntrainPredict = model.predict(trainX, batch_size=batch_size)\nmodel.reset_states()\ntestPredict = model.predict(testX, batch_size=batch_size)\nmodel.reset_states()\n# invert predictions\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n# calculate root mean squared error\ntrainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))\n# shift train predictions for plotting\ntrainPredictPlot = np.empty_like(dataset)\ntrainPredictPlot[:, :] = np.nan\ntrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n# shift test predictions for plotting\ntestPredictPlot = np.empty_like(dataset)\ntestPredictPlot[:, :] = np.nan\ntestPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n# plot baseline and predictions\nplt.plot(scaler.inverse_transform(dataset))\nplt.plot(trainPredictPlot)\nplt.plot(testPredictPlot)\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mRnZZrDzNZNf","outputId":"dc1d3e75-c6a6-41df-fa3b-d04d54126b96","execution":{"iopub.status.busy":"2023-11-01T10:03:13.503510Z","iopub.execute_input":"2023-11-01T10:03:13.504085Z","iopub.status.idle":"2023-11-01T10:06:41.861866Z","shell.execute_reply.started":"2023-11-01T10:03:13.504012Z","shell.execute_reply":"2023-11-01T10:06:41.860688Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"84/84 - 4s - loss: 0.0050 - 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- 573ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.6012e-04 - 617ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.5635e-04 - 574ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.5283e-04 - 531ms/epoch - 6ms/step\n84/84 - 1s - loss: 9.4953e-04 - 561ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.4643e-04 - 571ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.4349e-04 - 525ms/epoch - 6ms/step\n84/84 - 1s - loss: 9.4070e-04 - 551ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.3804e-04 - 574ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.3549e-04 - 566ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.3302e-04 - 552ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.3063e-04 - 574ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.2829e-04 - 582ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.2600e-04 - 577ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.2374e-04 - 610ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.2149e-04 - 583ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.1927e-04 - 602ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.1705e-04 - 592ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.1483e-04 - 616ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.1261e-04 - 711ms/epoch - 8ms/step\n84/84 - 1s - loss: 9.1038e-04 - 689ms/epoch - 8ms/step\n84/84 - 1s - loss: 9.0814e-04 - 573ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.0589e-04 - 631ms/epoch - 8ms/step\n84/84 - 1s - loss: 9.0363e-04 - 609ms/epoch - 7ms/step\n84/84 - 1s - loss: 9.0136e-04 - 599ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.9908e-04 - 590ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.9678e-04 - 565ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.9447e-04 - 566ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.9216e-04 - 586ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.8983e-04 - 561ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.8750e-04 - 576ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.8516e-04 - 622ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.8282e-04 - 556ms/epoch - 7ms/step\n84/84 - 1s - loss: 8.8047e-04 - 541ms/epoch - 6ms/step\n84/84 [==============================] - 1s 3ms/step\n35/35 [==============================] - 0s 3ms/step\nTrain Score: 14.97 RMSE\nTest Score: 131.63 RMSE\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Summary\nIn this tutorial, you discovered how to develop LSTM \n\nrecurrent neural networks for time series prediction in Python with the Keras deep learning network.\n\nSpecifically, you learned:\n\n*   About the international airline passenger time series prediction problem\n*   How to create an LSTM for a regression and a window formulation of the time series problem\n*   How to create an LSTM with a time step formulation of the time series problem\n*   How to create an LSTM with state and stacked LSTMs with state to learn long sequences","metadata":{"id":"wLhvc98wO0sT"}}]}