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Details for:
Brownlee J. Introduction to Time Series Forecasting..Python 2020
brownlee j introduction time series forecasting python 2020
Type:
E-books
Files:
1
Size:
6.4 MB
Uploaded On:
July 31, 2020, 10:37 a.m.
Added By:
andryold1
Seeders:
0
Leechers:
0
Info Hash:
5F6E0B81A78D5896CE880090F12C0F8A5ED3B874
Get This Torrent
Textbook in PDF format This book will show you how to make predictions on univariate time series problems using the tools in the Python ecosystem. Time series is an important and underserved topic in applied machine learning, Python is the growing platform for machine learning and predictive modeling, and this book unlocks time series for Python. Fundamentals Python Environment What is Time Series Forecasting? Time Series as Supervised Learning Data Preparation Load and Explore Time Series Data Basic Feature Engineering Data Visualization Resampling and Interpolation Power Transforms Moving Average Smoothing Temporal Structure A Gentle Introduction to White Noise A Gentle Introduction to the Random Walk Decompose Time Series Data Use and Remove Use and Remove Seasonality Stationarity in Time Series Data Evaluate Models Backtest Forecast Models Forecasting Performance Measures Persistence Model for Forecasting Visualize Residual Forecast Errors Reframe Time Series Forecasting Problems Forecast Models A Gentle Introduction to the Box-Jenkins Method Autoregression Models for Forecasting Moving Average Models for Forecasting ARIMA Model for Forecasting Autocorrelation and Partial Autocorrelation Grid Search ARIMA Model Hyperparameters Save Models and Make Predictions Forecast Condence Intervals Projects Time Series Forecast Projects Project: Monthly Armed Robberies in Boston Project: Annual Water Usage in Baltimore Project: Monthly Sales of French Champagne Conclusions How Far You Have Come Further Reading Appendixes Standard Time Series Datasets Workaround for Saving ARIMA Models
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Brownlee J. Introduction to Time Series Forecasting with Python 2020.pdf
6.4 MB
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