Vishnu Nandakumar Vishnunkumar
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Vishnunkumar
/ bubble_chart_ict.html
Created
November 28, 2020 11:24
plotly_bubble_chart_ict values of premier league players
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| <html> | |
| <head><meta charset="utf-8" /></head> | |
| <body> | |
| <div> <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script> | |
| <script type="text/javascript">/** | |
| * plotly.js v1.56.0 | |
| * Copyright 2012-2020, Plotly, Inc. | |
| * All rights reserved. | |
| * Licensed under the MIT license | |
| */ |
Vishnunkumar
/ one time forecast.py
Created
November 27, 2020 07:38
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| df_es = df_test.copy() | |
| df_es['preds'] = 0 | |
| p = len(df_test) | |
| model = ExponentialSmoothing(np.asarray(df_train['Page.Loads']),seasonal_periods=365, seasonal='add', trend='add') | |
| model_fit = model.fit() | |
| preds = model_fit.forecast(p) | |
| df_es['preds'] = preds |
Vishnunkumar
/ rolling_forecast.py
Created
November 27, 2020 07:37
Rolling training and forecast function
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| def rolling_forecast_es(df, df_test, p): | |
| """ | |
| Does rolling training and forecast for one week at a time | |
| """ | |
| df_test['preds'] = 0 | |
| for k in range(0, int(len(df_test)/p)): | |
| model = ExponentialSmoothing(np.asarray(df['Page.Loads'].iloc[:ix2 + (k)*p]),seasonal_periods=365, seasonal='add', trend='add') | |
| model_fit = model.fit() | |
| preds = model_fit.forecast(p) |
Vishnunkumar
/ main_es.py
Created
November 27, 2020 07:26
Time series forecasting using Exponential smoothing
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| from statsmodels.tsa.api import ExponentialSmoothing seasonal_decompose | |
| import statsmodels | |
| import pandas as pd | |
| import numpy as np | |
| import datetime | |
| from sklearn import metrics | |
| def preprocess(df): | |
| """ | |
| Preprocess the dataframe to required timeseries format |
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