Time Series Forecasting in Python

Time Series Forecasting in Python
brief contents
contents
preface
acknowledgments
about this book
	Who should read this book?
	How this book is organized: A roadmap
	About the code
	liveBook discussion forum
	Author online
about the author
about the cover illustration
Part 1—Time waits for no one
	1 Understanding time series forecasting
		1.1 Introducing time series
			1.1.1 Components of a time series
		1.2 Bird’s-eye view of time series forecasting
			1.2.1 Setting a goal
			1.2.2 Determining what must be forecast to achieve your goal
			1.2.3 Setting the horizon of the forecast
			1.2.4 Gathering the data
			1.2.5 Developing a forecasting model
			1.2.6 Deploying to production
			1.2.7 Monitoring
			1.2.8 Collecting new data
		1.3 How time series forecasting is different from other regression tasks
			1.3.1 Time series have an order
			1.3.2 Time series sometimes do not have features
		1.4 Next steps
	2 A naive prediction of the future
		2.1 Defining a baseline model
		2.2 Forecasting the historical mean
			2.2.1 Setup for baseline implementations
			2.2.2 Implementing the historical mean baseline
		2.3 Forecasting last year’s mean
		2.4 Predicting using the last known value
		2.5 Implementing the naive seasonal forecast
		2.6 Next steps
		Summary
	3 Going on a random walk
		3.1 The random walk process
			3.1.1 Simulating a random walk process
		3.2 Identifying a random walk
			3.2.1 Stationarity
			3.2.2 Testing for stationarity
			3.2.3 The autocorrelation function
			3.2.4 Putting it all together
			3.2.5 Is GOOGL a random walk?
		3.3 Forecasting a random walk
			3.3.1 Forecasting on a long horizon
			3.3.2 Forecasting the next timestep
		3.4 Next steps
		3.5 Exercises
			3.5.1 Simulate and forecast a random walk
			3.5.2 Forecast the daily closing price of GOOGL
			3.5.3 Forecast the daily closing price of a stock of your choice
		Summary
Part 2—Forecasting with statistical models
	4 Modeling a moving average process
		4.1 Defining a moving average process
			4.1.1 Identifying the order of a moving average process
		4.2 Forecasting a moving average process
		4.3 Next steps
		4.4 Exercises
			4.4.1 Simulate an MA(2) process and make forecasts
			4.4.2 Simulate an MA(q) process and make forecasts
		Summary
	5 Modeling an autoregressive process
		5.1 Predicting the average weekly foot traffic in a retail store
		5.2 Defining the autoregressive process
		5.3 Finding the order of a stationary autoregressive process
			5.3.1 The partial autocorrelation function (PACF)
		5.4 Forecasting an autoregressive process
		5.5 Next steps
		5.6 Exercises
			5.6.1 Simulate an AR(2) process and make forecasts
			5.6.2 Simulate an AR(p) process and make forecasts
		Summary
	6 Modeling complex time series
		6.1 Forecasting bandwidth usage for data centers
		6.2 Examining the autoregressive moving average process
		6.3 Identifying a stationary ARMA process
		6.4 Devising a general modeling procedure
			6.4.1 Understanding the Akaike information criterion (AIC)
			6.4.2 Selecting a model using the AIC
			6.4.3 Understanding residual analysis
			6.4.4 Performing residual analysis
		6.5 Applying the general modeling procedure
		6.6 Forecasting bandwidth usage
		6.7 Next steps
		6.8 Exercises
			6.8.1 Make predictions on the simulated ARMA(1,1) process
			6.8.2 Simulate an ARMA(2,2) process and make forecasts
		Summary
	7 Forecasting non-stationary time series
		7.1 Defining the autoregressive integrated moving average model
		7.2 Modifying the general modeling procedure to account for non-stationary series
		7.3 Forecasting a non-stationary times series
		7.4 Next steps
		7.5 Exercises
			7.5.1 Apply the ARIMA(p,d,q) model on the datasets from chapters 4, 5, and 6
		Summary
	8 Accounting for seasonality
		8.1 Examining the SARIMA(p,d,q)(P,D,Q)m model
		8.2 Identifying seasonal patterns in a time series
		8.3 Forecasting the number of monthly air passengers
			8.3.1 Forecasting with an ARIMA(p,d,q) model
			8.3.2 Forecasting with a SARIMA(p,d,q)(P,D,Q)m model
			8.3.3 Comparing the performance of each forecasting method
		8.4 Next steps
		8.5 Exercises
			8.5.1 Apply the SARIMA(p,d,q)(P,D,Q)m model on the Johnson & Johnson dataset
		Summary
	9 Adding external variables to our model
		9.1 Examining the SARIMAX model
			9.1.1 Exploring the exogenous variables of the US macroeconomics dataset
			9.1.2 Caveat for using SARIMAX
		9.2 Forecasting the real GDP using the SARIMAX model
		9.3 Next steps
		9.4 Exercises
			9.4.1 Use all exogenous variables in a SARIMAX model to predict the real GDP
		Summary
	10 Forecasting multiple time series
		10.1 Examining the VAR model
		10.2 Designing a modeling procedure for the VAR(p) model
			10.2.1 Exploring the Granger causality test
		10.3 Forecasting real disposable income and real consumption
		10.4 Next steps
		10.5 Exercises
			10.5.1 Use a VARMA model to predict realdpi and realcons
			10.5.2 Use a VARMAX model to predict realdpi and realcons
		Summary
	11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
		11.1 Importing the required libraries and loading the data
		11.2 Visualizing the series and its components
		11.3 Modeling the data
			11.3.1 Performing model selection
			11.3.2 Conducting residual analysis
		11.4 Forecasting and evaluating the model’s performance
		Next steps
Part 3—Large-scale forecasting with deep learning
	12 Introducing deep learning for time series forecasting
		12.1 When to use deep learning for time series forecasting
		12.2 Exploring the different types of deep learning models
		12.3 Getting ready to apply deep learning for forecasting
			12.3.1 Performing data exploration
			12.3.2 Feature engineering and data splitting
		12.4 Next steps
		12.5 Exercise
		Summary
	13 Data windowing and creating baselines for deep learning
		13.1 Creating windows of data
			13.1.1 Exploring how deep learning models are trained for time series forecasting
			13.1.2 Implementing the DataWindow class
		13.2 Applying baseline models
			13.2.1 Single-step baseline model
			13.2.2 Multi-step baseline models
			13.2.3 Multi-output baseline model
		13.3 Next steps
		13.4 Exercises
		Summary
	14 Baby steps with deep learning
		14.1 Implementing a linear model
			14.1.1 Implementing a single-step linear model
			14.1.2 Implementing a multi-step linear model
			14.1.3 Implementing a multi-output linear model
		14.2 Implementing a deep neural network
			14.2.1 Implementing a deep neural network as a single-step model
			14.2.2 Implementing a deep neural network as a multi-step model
			14.2.3 Implementing a deep neural network as a multi-output model
		14.3 Next steps
		14.4 Exercises
		Summary
	15 Remembering the past with LSTM
		15.1 Exploring the recurrent neural network (RNN)
		15.2 Examining the LSTM architecture
			15.2.1 The forget gate
			15.2.2 The input gate
			15.2.3 The output gate
		15.3 Implementing the LSTM architecture
			15.3.1 Implementing an LSTM as a single-step model
			15.3.2 Implementing an LSTM as a multi-step model
			15.3.3 Implementing an LSTM as a multi-output model
		15.4 Next steps
		15.5 Exercises
		Summary
	16 Filtering a time series with CNN
		16.1 Examining the convolutional neural network (CNN)
		16.2 Implementing a CNN
			16.2.1 Implementing a CNN as a single-step model
			16.2.2 Implementing a CNN as a multi-step model
			16.2.3 Implementing a CNN as a multi-output model
		16.3 Next steps
		16.4 Exercises
		Summary
	17 Using predictions to make more predictions
		17.1 Examining the ARLSTM architecture
		17.2 Building an autoregressive LSTM model
		17.3 Next steps
		17.4 Exercises
		Summary
	18 Capstone: Forecasting the electric power consumption of a household
		18.1 Understanding the capstone project
			18.1.1 Objective of this capstone project
		18.2 Data wrangling and preprocessing
			18.2.1 Dealing with missing data
			18.2.2 Data conversion
			18.2.3 Data resampling
		18.3 Feature engineering
			18.3.1 Removing unnecessary columns
			18.3.2 Identifying the seasonal period
			18.3.3 Splitting and scaling the data
		18.4 Preparing for modeling with deep learning
			18.4.1 Initial setup
			18.4.2 Defining the DataWindow class
			18.4.3 Utility function to train our models
		18.5 Modeling with deep learning
			18.5.1 Baseline models
			18.5.2 Linear model
			18.5.3 Deep neural network
			18.5.4 Long short-term memory (LSTM) model
			18.5.5 Convolutional neural network (CNN)
			18.5.6 Combining a CNN with an LSTM
			18.5.7 The autoregressive LSTM model
			18.5.8 Selecting the best model
		18.6 Next steps
Part 4—Automating forecasting at scale
	19 Automating time series forecasting with Prophet
		19.1 Overview of the automated forecasting libraries
		19.2 Exploring Prophet
		19.3 Basic forecasting with Prophet
		19.4 Exploring Prophet’s advanced functionality
			19.4.1 Visualization capabilities
			19.4.2 Cross-validation and performance metrics
			19.4.3 Hyperparameter tuning
		19.5 Implementing a robust forecasting process with Prophet
			19.5.1 Forecasting project: Predicting the popularity of “chocolate” searches on Google
			19.5.2 Experiment: Can SARIMA do better?
		19.6 Next steps
		19.7 Exercises
			19.7.1 Forecast the number of air passengers
			19.7.2 Forecast the volume of antidiabetic drug prescriptions
			19.7.3 Forecast the popularity of a keyword on Google Trends
		Summary
	20 Capstone: Forecasting the monthly average retail price of steak in Canada
		20.1 Understanding the capstone project
			20.1.1 Objective of the capstone project
		20.2 Data preprocessing and visualization
		20.3 Modeling with Prophet
		20.4 Optional: Develop a SARIMA model
		20.5 Next steps
	21 Going above and beyond
		21.1 Summarizing what you’ve learned
			21.1.1 Statistical methods for forecasting
			21.1.2 Deep learning methods for forecasting
			21.1.3 Automating the forecasting process
		21.2 What if forecasting does not work?
		21.3 Other applications of time series data
		21.4 Keep practicing
Appendix—Installation instructions
	Installing Anaconda
	Python
	Jupyter Notebooks
	GitHub Repository
	Installing Prophet
	Installing libraries in Anaconda
index
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