Time Series Forecasting in Python (MEAP v03)

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the technology You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you'll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Capstone: Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Capstone: Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond 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 Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. Youll explore interesting real-world datasets like Googles daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology You can predict the futurewith a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, youll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and youll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canadas largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the Technology You can predict the future--with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the Book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you'll soon be ready to build your own accurate, insightful forecasts. What's Inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the Reader For data scientists familiar with Python and TensorFlow. About the Author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks. Quotes The importance of time series analysis cannot be overstated. This book provides key techniques to deal with time series data in real-world applications. Indispensable. - Amaresh Rajasekharan, IBM Marco Peixeiro presents concepts clearly using interesting examples and illustrative plots. You'll be up and running quickly using the power of Python. - Ariel Andres, MD Financial Management What caught my attention were the practical examples immediately applicable to real life. He explains complex topics without the excess of mathematical formalism. - Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland