Time Series Forecasting in Python

Time Series Forecasting in Python MEAP V03
Copyright
welcome
brief contents
Chapter 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
	1.3 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
	1.5 Summary
Chapter 2: A naïve prediction of the future
	2.1 Defining a baseline model
	2.2 Forecasting the historical mean
	2.3 Forecasting last year’s mean
	2.4 Predicting using the last known value
	2.5 Implementing the naïve seasonal forecast
	2.6 Next steps
	2.7 Summary
Chapter 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 Exercises
		3.4.1 Easy: simulate and forecast a random walk
		3.4.2 Medium: Forecast the daily closing price of GOOGL
		3.4.3 Hard: Forecast the daily closing price of a stock of your choice
	3.5 Next steps
	3.6 Summary
Chapter 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 Easy: Simulate a MA(2) process and make forecasts
		4.4.2 Medium: Simulate a MA(q) process and make forecasts
	4.5 Summary
Chapter 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 Easy: Simulate an AR(2) process and make forecasts
		5.6.2 Medium: Simulate an AR(p) process and make forecasts
	5.7 Summary
Chapter 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’s Information Criterion (AIC)
		6.4.2 Selecting a model using the AIC
		6.4.3 Understanding residuals analysis
		6.4.4 Performing residuals analysis
	6.5 Applying the general modeling procedure
	6.6 Forecasting bandwidth usage
	6.7 Next steps
	6.8 Exercises
		6.8.1 Easy: Make predictions on the simulated ARMA(1,1) process
		6.8.2 Medium: Simulate an ARMA(2,2) process and make forecasts
	6.9 Summary
Chapter 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 nonstationary
	7.3 Forecasting a non-stationary times series
	7.4 Next steps
	7.5 Exercises
		7.5.1 Medium: Apply the ARIMA(p,d,q) model on the datasets from chapters 4, 5 or 6.
	7.6 Summary
Chapter 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 performance of each forecasting method
	8.4 Next steps
	8.5 Exercises
		8.5.1 Medium: Apply the SARIMA(p,d,q)(P,D,Q)m model on the Johnson & Johnson dataset
	8.6 Summary
Chapter 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 of using SARIMAX
	9.2 Forecasting the real GDP using the SARIMAX model
	9.3 Next steps
	9.4 Exercises
		9.4.1 Easy: Use all exogenous variables in a SARIMAX model to predict the real GDP
	9.5 Summary