Introduction to Time Series Forecasting with Python - How to Prepare Data and Develop Models to Predict the Future

Copyright
Welcome
I Fundamentals
	Python Environment
		Why Python?
		Python Libraries for Time Series
		Python Ecosystem Installation
		Summary
	What is Time Series Forecasting?
		Time Series
		Time Series Nomenclature
		Describing vs. Predicting
		Components of Time Series
		Concerns of Forecasting
		Examples of Time Series Forecasting
		Summary
	Time Series as Supervised Learning
		Supervised Machine Learning
		Sliding Window
		Sliding Window With Multivariates
		Sliding Window With Multiple Steps
		Summary
II Data Preparation
	Load and Explore Time Series Data
		Daily Female Births Dataset
		Load Time Series Data
		Exploring Time Series Data
		Summary
	Basic Feature Engineering
		Feature Engineering for Time Series
		Goal of Feature Engineering
		Minimum Daily Temperatures Dataset
		Date Time Features
		Lag Features
		Rolling Window Statistics
		Expanding Window Statistics
		Summary
	Data Visualization
		Time Series Visualization
		Minimum Daily Temperatures Dataset
		Line Plot
		Histogram and Density Plots
		Box and Whisker Plots by Interval
		Heat Maps
		Lag Scatter Plots
		Autocorrelation Plots
		Summary
	Resampling and Interpolation
		Resampling
		Shampoo Sales Dataset
		Upsampling Data
		Downsampling Data
		Summary
	Power Transforms
		Airline Passengers Dataset
		Square Root Transform
		Log Transform
		Box-Cox Transform
		Summary
	Moving Average Smoothing
		Moving Average Smoothing
		Data Expectations
		Daily Female Births Dataset
		Moving Average as Data Preparation
		Moving Average as Feature Engineering
		Moving Average as Prediction
		Summary
III Temporal Structure
	A Gentle Introduction to White Noise
		What is a White Noise?
		Why Does it Matter?
		Is your Time Series White Noise?
		Example of White Noise Time Series
		Summary
	A Gentle Introduction to the Random Walk
		Random Series
		Random Walk
		Random Walk and Autocorrelation
		Random Walk and Stationarity
		Predicting a Random Walk
		Is Your Time Series a Random Walk?
		Summary
	Decompose Time Series Data
		Time Series Components
		Combining Time Series Components
		Decomposition as a Tool
		Automatic Time Series Decomposition
		Summary
	Use and Remove Trends
		Trends in Time Series
		Shampoo Sales Dataset
		Detrend by Differencing
		Detrend by Model Fitting
		Summary
	Use and Remove Seasonality
		Seasonality in Time Series
		Minimum Daily Temperatures Dataset
		Seasonal Adjustment with Differencing
		Seasonal Adjustment with Modeling
		Summary
	Stationarity in Time Series Data
		Stationary Time Series
		Non-Stationary Time Series
		Types of Stationary Time Series
		Stationary Time Series and Forecasting
		Checks for Stationarity
		Summary Statistics
		Augmented Dickey-Fuller test
		Summary
IV Evaluate Models
	Backtest Forecast Models
		Model Evaluation
		Monthly Sunspots Dataset
		Train-Test Split
		Multiple Train-Test Splits
		Walk Forward Validation
		Summary
	Forecasting Performance Measures
		Forecast Error (or Residual Forecast Error)
		Mean Forecast Error (or Forecast Bias)
		Mean Absolute Error
		Mean Squared Error
		Root Mean Squared Error
		Summary
	Persistence Model for Forecasting
		Forecast Performance Baseline
		Persistence Algorithm
		Shampoo Sales Dataset
		Persistence Algorithm Steps
		Summary
	Visualize Residual Forecast Errors
		Residual Forecast Errors
		Daily Female Births Dataset
		Persistence Forecast Model
		Residual Line Plot
		Residual Summary Statistics
		Residual Histogram and Density Plots
		Residual Q-Q Plot
		Residual Autocorrelation Plot
		Summary
	Reframe Time Series Forecasting Problems
		Benefits of Reframing Your Problem
		Minimum Daily Temperatures Dataset
		Naive Time Series Forecast
		Regression Framings
		Classification Framings
		Time Horizon Framings
		Summary
V Forecast Models
	A Gentle Introduction to the Box-Jenkins Method
		Autoregressive Integrated Moving Average Model
		Box-Jenkins Method
		Identification
		Estimation
		Diagnostic Checking
		Summary
	Autoregression Models for Forecasting
		Autoregression
		Autocorrelation
		Minimum Daily Temperatures Dataset
		Quick Check for Autocorrelation
		Autocorrelation Plots
		Persistence Model
		Autoregression Model
		Summary
	Moving Average Models for Forecasting
		Model of Residual Errors
		Daily Female Births Dataset
		Persistence Forecast Model
		Autoregression of Residual Error
		Correct Predictions with a Model of Residuals
		Summary
	ARIMA Model for Forecasting
		Autoregressive Integrated Moving Average Model
		Shampoo Sales Dataset
		ARIMA with Python
		Rolling Forecast ARIMA Model
		Summary
	Autocorrelation and Partial Autocorrelation
		Minimum Daily Temperatures Dataset
		Correlation and Autocorrelation
		Partial Autocorrelation Function
		Intuition for ACF and PACF Plots
		Summary
	Grid Search ARIMA Model Hyperparameters
		Grid Searching Method
		Evaluate ARIMA Model
		Iterate ARIMA Parameters
		Shampoo Sales Case Study
		Daily Female Births Case Study
		Extensions
		Summary
	Save Models and Make Predictions
		Process for Making a Prediction
		Daily Female Births Dataset
		Select Time Series Forecast Model
		Finalize and Save Time Series Forecast Model
		Make a Time Series Forecast
		Update Forecast Model
		Extensions
		Summary
	Forecast Confidence Intervals
		ARIMA Forecast
		Daily Female Births Dataset
		Forecast Confidence Interval
		Interpreting the Confidence Interval
		Plotting the Confidence Interval
		Summary
VI Projects
	Time Series Forecast Projects
		5-Step Forecasting Task
		Iterative Forecast Development Process
		Suggestions and Tips
		Summary
	Project: Monthly Armed Robberies in Boston
		Overview
		Problem Description
		Test Harness
		Persistence
		Data Analysis
		ARIMA Models
		Model Validation
		Extensions
		Summary
	Project: Annual Water Usage in Baltimore
		Overview
		Problem Description
		Test Harness
		Persistence
		Data Analysis
		ARIMA Models
		Model Validation
		Summary
	Project: Monthly Sales of French Champagne
		Overview
		Problem Description
		Test Harness
		Persistence
		Data Analysis
		ARIMA Models
		Model Validation
		Summary
VII Conclusions
	How Far You Have Come
	Further Reading
		Applied Time Series
		Time Series
		Machine Learning
		Getting Help
		Contact the Author
VIII Appendix
	Standard Time Series Datasets
		Shampoo Sales Dataset
		Minimum Daily Temperatures Dataset
		Monthly Sunspots Dataset
		Daily Female Births Dataset
		Airline Passengers Dataset
	Workaround for Saving ARIMA Models
		Daily Female Births Dataset
		Python Environment
		ARIMA Model Save Bug
		ARIMA Model Save Bug Workaround
		Summary