| !pip install neuralprophet | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from neuralprophet import NeuralProphet | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| import os | |
| for dirname, _, filesnames in os.walk('yourstockdata.csv') | |
| for filenames in filesnames: | |
| print(os.path.join(dirname, filename)) | |
| df = pd.read_csv('youstockdata.csv') | |
| df.head() | |
| df.info() | |
| df['Date'] = pd.to_datetime(df['Date']) | |
| df.dtypes | |
| df = df[['Date', 'Close']] | |
| df.head() | |
| df.columns = ['ds', 'y'] | |
| df.head() | |
| plt.plot(df['ds'], df['y'], label='actual', c='g') | |
| plt.title('Stock Data') | |
| plt.xlabel('Date') | |
| plt.ylabel('Stock Price') | |
| plt.show() | |
| model = NeuralProphet( | |
| batch_size=16 | |
| ) | |
| model.fit(df) | |
| future = model.make_future_dataframe(df, periods=365) | |
| forecast = model.predict(future) | |
| forecast | |
| actual_prediction = model.predict(df) | |
| plt.plot(df['ds'], df['y'], label='actual', c='g') | |
| plt.plot(actual_prediction['ds'], actual_prediction['yhat1'], label='prediction_actual', c='r') | |
| plt.plot(forecast['ds'], forecast['yhat1'], label='future_prediction', c='b') | |
| plt.xlabel('Date') | |
| plt.ylabel('Stock Price') | |
| plt.legend() | |
| plt.show() | |
| model.plot_components(forecast) | |