Brownlee J Introduction to Time Series Forecasting Python 2020 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