neuralforecast works, statsforecast placeholder is copy of neuralforecast.

v2/test_predictor_neuralforecast.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+
+from neuralforecast import NeuralForecast
+from neuralforecast.models import LSTM, NHITS, RNN, PatchTST
+
+
+data = pd.read_csv("terheles_fixed.tsv", sep="\t")
+data['ds'] = pd.to_datetime(data['Korrigált időpont'])
+data['y'] = data['Hatásos teljesítmény']
+
+data = data[['ds', 'y']]
+data['unique_id'] = 1
+
+data = data[data['ds'] < '2019-09-01']
+Y_df = data
+print(Y_df)
+
+
+horizon = 4 * 24 * 7 # 7 days
+
+# Try different hyperparmeters to improve accuracy.
+models = [
+            PatchTST(h=horizon,                    # Forecast horizon
+               input_size=2 * horizon,      # Length of input sequence
+               max_steps=20,                # Number of steps to train
+               scaler_type='standard'),       # Type of scaler to normalize data
+
+            NHITS(h=horizon,                   # Forecast horizon
+                input_size=2 * horizon,      # Length of input sequence
+                max_steps=100,               # Number of steps to train
+                n_freq_downsample=[2, 1, 1]) # Downsampling factors for each stack output
+          ]
+'''
+            LSTM(h=horizon,                    # Forecast horizon
+               max_steps=100,                # Number of steps to train
+               scaler_type='standard',       # Type of scaler to normalize data
+               encoder_hidden_size=64,       # Defines the size of the hidden state of the LSTM
+               decoder_hidden_size=64,),     # Defines the number of hidden units of each layer of the MLP decoder
+'''
+
+
+nf = NeuralForecast(models=models, freq='15min')
+
+shorter_Y_df = Y_df[Y_df['ds'] < '2019-08-01']
+print("-=======-")
+print(len(shorter_Y_df))
+print(shorter_Y_df)
+nf.fit(df=shorter_Y_df)
+
+Y_hat_df = nf.predict()
+
+print(Y_df)
+
+Y_hat_df = Y_hat_df.reset_index()
+
+fig, ax = plt.subplots(1, 1, figsize = (20, 7))
+
+# plot_df = pd.concat([Y_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes
+# plot_df[['y', 'LSTM', 'NHITS']].plot(ax=ax, linewidth=2)
+
+plot_Y_df = Y_df[Y_df['ds'] > '2019-07-01']
+plot_Y_df = plot_Y_df.set_index('ds')[['y']]
+plot_Y_df.plot(ax=ax, linewidth=1)
+Y_hat_df.set_index('ds')[['PatchTST', 'NHITS']].plot(ax=ax, linewidth=1)
+
+
+ax.set_title('AirPassengers Forecast', fontsize=22)
+ax.set_ylabel('Monthly Passengers', fontsize=20)
+ax.set_xlabel('Timestamp [t]', fontsize=20)
+ax.legend(prop={'size': 15})
+ax.grid()
+
+plt.savefig("neuralforecast.pdf")

v2/test_predictor_statsforecast.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+
+from neuralforecast import NeuralForecast
+from neuralforecast.models import LSTM, NHITS, RNN, PatchTST
+
+
+data = pd.read_csv("terheles_fixed.tsv", sep="\t")
+data['ds'] = pd.to_datetime(data['Korrigált időpont'])
+data['y'] = data['Hatásos teljesítmény']
+
+data = data[['ds', 'y']]
+data['unique_id'] = 1
+
+data = data[data['ds'] < '2019-09-01']
+Y_df = data
+print(Y_df)
+
+
+horizon = 4 * 24 * 7 # 7 days
+
+# Try different hyperparmeters to improve accuracy.
+models = [
+            PatchTST(h=horizon,                    # Forecast horizon
+               input_size=2 * horizon,      # Length of input sequence
+               max_steps=20,                # Number of steps to train
+               scaler_type='standard'),       # Type of scaler to normalize data
+
+            NHITS(h=horizon,                   # Forecast horizon
+                input_size=2 * horizon,      # Length of input sequence
+                max_steps=100,               # Number of steps to train
+                n_freq_downsample=[2, 1, 1]) # Downsampling factors for each stack output
+          ]
+'''
+            LSTM(h=horizon,                    # Forecast horizon
+               max_steps=100,                # Number of steps to train
+               scaler_type='standard',       # Type of scaler to normalize data
+               encoder_hidden_size=64,       # Defines the size of the hidden state of the LSTM
+               decoder_hidden_size=64,),     # Defines the number of hidden units of each layer of the MLP decoder
+'''
+
+
+nf = NeuralForecast(models=models, freq='15min')
+
+shorter_Y_df = Y_df[Y_df['ds'] < '2019-08-01']
+print("-=======-")
+print(len(shorter_Y_df))
+print(shorter_Y_df)
+nf.fit(df=shorter_Y_df)
+
+Y_hat_df = nf.predict()
+
+print(Y_df)
+
+Y_hat_df = Y_hat_df.reset_index()
+
+fig, ax = plt.subplots(1, 1, figsize = (20, 7))
+
+# plot_df = pd.concat([Y_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes
+# plot_df[['y', 'LSTM', 'NHITS']].plot(ax=ax, linewidth=2)
+
+plot_Y_df = Y_df[Y_df['ds'] > '2019-07-01']
+plot_Y_df = plot_Y_df.set_index('ds')[['y']]
+plot_Y_df.plot(ax=ax, linewidth=1)
+Y_hat_df.set_index('ds')[['PatchTST', 'NHITS']].plot(ax=ax, linewidth=1)
+
+
+ax.set_title('AirPassengers Forecast', fontsize=22)
+ax.set_ylabel('Monthly Passengers', fontsize=20)
+ax.set_xlabel('Timestamp [t]', fontsize=20)
+ax.legend(prop={'size': 15})
+ax.grid()
+
+plt.savefig("neuralforecast.pdf")