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"""Vecna.159 |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1gX09qWUyT9sTqHSCPCRbeAU3veDQ9KOC |
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""" |
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!pip install neuralprophet |
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import numpy as np |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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from neuralprophet import NeuralProphet |
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import warnings |
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warnings.filterwarnings('ignore') |
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import os |
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for dirname, _, filenames in os.walk('/content/Meta Dataset.csv'): |
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for filename in filenames: |
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print(os.path.join(dirname, filename)) |
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df = pd.read_csv('/content/Meta Dataset.csv') |
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df.head() |
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df.info() |
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df['Date'] = pd.to_datetime(df['Date']) |
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df.dtypes |
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df = df[['Date', 'Close']] |
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df.head() |
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df.columns = ['ds', 'y'] |
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df.head() |
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plt.plot(df['ds'], df['y'], label='actual', c='g') |
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plt.title('Meta Stock Prices Over TIme') |
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plt.xlabel('Date') |
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plt.ylabel('Stock Price') |
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plt.show() |
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model = NeuralProphet( |
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batch_size=16 |
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) |
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model.fit(df) |
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future = model.make_future_dataframe(df, periods=365) |
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forecast = model.predict(future) |
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forecast |
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actual_prediction = model.predict(df) |
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plt.plot(df['ds'], df['y'], label='actual', c='g') |
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plt.plot(actual_prediction['ds'], actual_prediction['yhat1'], label='prediction_actual', c='r') |
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plt.plot(forecast['ds'], forecast['yhat1'], label='future_prediction', c='b') |
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plt.xlabel('Date') |
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plt.ylabel('Stock Price') |
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plt.legend() |
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plt.show() |
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model.plot_components(forecast) |
