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Time Series Forecasting in Python
- Description
- Product Details
- About the Author
- Table of Contents
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.
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 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 Read Full Overview
ISBN-13: 9781617299889
Media Type: Paperback
Publisher: Manning
Publication Date: 10-04-2022
Pages: 456
Product Dimensions: 7.38(w) x 9.25(h) x 1.20(d)
Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks. He is an active contributor to Towards Data Science, an instructor on Udemy, and on YouTube in collaboration with freeCodeCamp.
Preface xvii Acknowledgments xix About this book xx About the author xxiv About the cover illustration xxv Part 1 Time waits for no one 1 1 Understanding time series forecasting 3 1.1 Introducing time series 4 Components of a time series 5 1.2 Bird's-eye view of time series forecasting 8 Setting a goal 9 Determining what must be forecast to achieve your goal 9 Setting the horizon of the forecast 10 Gathering the data 10 Developing a forecasting model 10 Deploying to production 11 Monitoring 11 Collecting new data 11 1.3 How time series forecasting is different from other regression tasks 12 Time series have an order 12 Time series sometimes do not have features 13 1.4 Next steps 13 2 A naive prediction of the future 14 2.1 Defining a baseline model 16 2.2 Forecasting the historical mean 17 Setup for baseline implementations 17 Implementing the historical mean baseline 19 2.3 Forecasting last year's mean 23 2.4 Predicting using the last known value 25 2.5 Implementing the naive seasonal forecast 26 2.6 Next steps 28 3 Going on a random walk 30 3.1 The random walk process 31 Simulating a random walk process 32 3.2 Identifying a random walk 35 Stationarity 36 Testing for stationarity 38 The autocorrelation function 41 Putting it all together 42 Is GOOGL a random walk? 45 3.3 Forecasting a random walk 47 Forecasting on a long horizon 48 Forecasting the next timestep 52 3.4 Next steps 55 3.5 Exercises 56 Simulate and forecast a random walk 56 Forecast the daily closing price of GOOGL 57 Forecast the daily closing price of a stock of your choice 57 Part 2 Forecasting with statistical models 59 4 Modeling a moving average process 61 4.1 Defining a moving average process 63 Identifying the order of a moving average process 64 4.2 Forecasting a moving average process 69 4.3 Next steps 78 4.4 Exercises 79 Simulate an MA(2) process and make forecasts 79 Simulate an MA(q) process and make forecasts 80 5 Modeling an autoregressive process 81 5.1 Predicting the average weekly foot traffic in a retail store 82 5.2 Defining the autoregressive process 84 5.3 Finding the order of a stationary autoregressive process 85 The partial autocorrelation function (PACF) 89 5.4 Forecasting an autoregressive process 92 5.5 Next steps 98 5.6 Exercises 99 Simulate an AR(2) process and make forecasts 99 Simulate an AR(p) process and make forecasts 100 6 Modeling complex time series 101 6.1 Forecasting bandwidth usage for data centers 102 6.2 Examining the autoregressive moving average process 105 6.3 Identifying a stationary ARMA process 106 6.4 Devising a general modeling procedure 111 Understanding the Akaike information criterion (AIC) 113 Selecting a model using the AIC 114 Understanding residual analysis 116 Performing residual analysis 121 6.5 Applying the general modeling procedure 125 6.6 Forecasting bandwidth usage 132 6.7 Next steps 136 6.8 Exercises 137 Make predictions on the simulated ARMA(1,1) process 137 Simulate an ARMA(2,2) process and make forecasts 137 7 Forecasting non-stationary time series 140 7.1 Defining the autoregressive integrated moving average model 142 7.2 Modifying the general modeling procedure to account for non-stationary series 143 7.3 Forecasting a non-stationary times series 145 7.4 Next steps 154 7.5 Exercises 154 Apply the ARIMA(p,d,q) model on the datasets from chapters 4, 5, and 6 154 8 Accounting for seasonality 156 8.1 Examining the SARIMA(p,d,q) (P,D,Q)m model 157 8.2 Identifying seasonal patterns in a time series 160 8.3 Forecasting the number of monthly air passengers 163 Forecasting with an ARIMA(p,d,q) model 165 Forecasting with a SARIMA(p,d,q)(P,D,Q)m model 171 Comparing the performance of each forecasting method 176 8.4 Next steps 178 8.5 Exercises 178 Apply the SARIMA(p,d,q)(P,D,Q)m model on the Johnson & Johnson dataset 178 9 Adding external variables to our model 180 9.1 Examining the SARIMAX model 182 Exploring the exogenous variables of the US macroeconomics dataset 183 Caveat for using SARIMAX 185 9.2 Forecasting the real GDP using the SARIMAX model 186 9.3 Next steps 195 9.4 Exercises 195 Use all exogenous variables in a SARIMAX model to predict the real GDP 195 10 Forecasting multiple time series 197 10.1 Examining the VAR model 199 10.2 Designing a modeling procedure for the VAR(p) model 201 Exploring the Granger causality test 201 10.3 Forecasting real disposable income and real consumption 203 10.4 Next steps 214 10.5 Exercises 214 Use a VARMA model to predict realdpi and realcons 214 Use a VARMAX model to predict realdpi and realcons 215 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia 216 11.1 Importing the required libraries and loading the data 218 11.2 Visualizing the series and its components 219 11.3 Modeling the data 220 Performing model selection 222 Conducting residual analysis 224 11.4 Forecasting and evaluating the model's performance 225 11.5 Next steps 229 Part 3 Large-scale forecasting with deep learning 231 12 Introducing deep learning for time series forecasting 233 12.1 When to use deep learning for time series forecasting 234 12.2 Exploring the different types of deep learning models 234 12.3 Getting ready to apply deep learning for forecasting 237 Performing data exploration 237 Feature engineering and data splitting 241 12.4 Next steps 246 12.5 Exercise 246 13 Data windowing and creating baselines for deep learning 248 13.1 Creating windows of data 249 Exploring how deep learning models are trained for time series forecasting 249 Implementing the Data Window class 253 13.2 Applying baseline models 260 Single-step baseline model 260 Multi-step baseline models 263 Multi-output baseline model 266 13.3 Next steps 268 13.4 Exercises 269 14 Baby steps with deep learning 270 14.1 Implementing a linear model 271 Implementing a single-step linear model 272 Implementing a multi-step linear model 274 Implementing a multi-output linear model 275 14.2 Implementing a deep neural network 276 Implementing a deep neural network as a single-step model 278 Implementing a deep neural network as a multi-step model 281 Implementing a deep neural network as a multi-output model 282 14.3 Next steps 284 14.4 Exercises 285 15 Remembering the past with LSTM 287 15.1 Exploring the recurrent neural network (RNN) 288 15.2 Examining the LSTM architecture 290 The forget gate 291 The input gate 292 The output gate 294 15.3 Implementing the LSTM architecture 295 Implementing an LSTM as a single-step model 295 Implementing an LSTM as a multi-step model 297 Implementing an LSTM as a multi-output model 299 15.4 Next steps 302 15.5 Exercises 303 16 Filtering a time series with CNN 305 16.1 Examining the convolutional neural network (CNN) 306 16.2 Implementing a CNN 309 Implementing a CNN as a single-step model 310 Implementing a CNN as a multi-step model 314 Implementing a CNN as a multi-output model 315 16.3 Next steps 317 16.4 Exercises 318 17 Using predictions to make more predictions 320 17.1 Examining the ARLSTM architecture 321 17.2 Building an autoregressive LSTM model 322 17.3 Next steps 327 17.4 Exercises 328 18 Capstone: Forecasting the electric power consumption of a household 329 18.1 Understanding the capstone project 330 Objective of this capstone project 331 18.2 Data wrangling and preprocessing 333 Dealing with missing data 334 Data conversion 335 Data resampling 335 18.3 Feature engineering 338 Removing unnecessary columns 338 Identifying the seasonal period 339 Splitting and scaling the data 341 18.4 Preparing for modeling with deep learning 342 Initial setup 342 Defining the DataWindow class 343 Utility junction to train our models 346 18.5 Modeling with deep learning 346 Baseline models 346 Linear model 349 Deep neural network 350 Long short-term memory (LSTM) model 351 Convolutional neural network (CNN) 351 Combining a CNN with an LSTM 354 The autoregressive LSTM model 355 Selecting the best model 356 18.6 Next steps 358 Part 4 Automating forecasting at scale 359 19 Automating time series forecasting with Prophet 361 19.1 Overview of the automated forecasting libraries 362 19.2 Exploring Prophet 363 19.3 Basic forecasting with Prophet 365 19.4 Exploring Prophet's advanced functionality 370 Visualization capabilities 370 Cross-validation and performance metrics 374 Hyperparameter tuning 379 19.5 Implementing a robust forecasting process with Prophet 381 Forecasting project: Predicting the popularity of "chocolate" searches on Google 381 Experiment: Can SARIMA do better? 389 19.6 Next steps 393 19.7 Exercises 394 Forecast the number of air passengers 394 Forecast the volume of antidiabetic drug prescriptions 394 Forecast the popularity of a keyword on Google Trends 394 20 Capstone: Forecasting the monthly average retail price of steak in Canada 396 20.1 Understanding the capstone project 397 Objective of the capstone project 397 20.2 Data preprocessing and visualization 398 20.3 Modeling with Prophet 400 20.4 Optional: Develop a SARIMA model 404 20.5 Next steps 409 21 Going above and beyond 410 21.1 Summarizing what you've learned 411 Statistical methods for forecasting 411 Deep learning methods for forecasting 412 Automating the forecasting process 413 21.2 What if forecasting does not work? 413 21.3 Other applications of time series data 415 21.4 Keep practicing 416 Appendix Installation instructions 418 Index 421Table of Contents