Book Image

Modern Time Series Forecasting with Python

By : Manu Joseph
5 (1)
Book Image

Modern Time Series Forecasting with Python

5 (1)
By: Manu Joseph

Overview of this book

We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.

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Table of Contents (26 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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Conventions used
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1
Part 1 – Getting Familiar with Time Series
Part 1 – Getting Familiar with Time Series
Free Chapter
2
Chapter 1: Introducing Time Series
Chapter 1: Introducing Time Series
Technical requirements
What is a time series?
Data-generating process (DGP)
What can we forecast?
Forecasting terminology
Summary
Further reading
3
Chapter 2: Acquiring and Processing Time Series Data
Chapter 2: Acquiring and Processing Time Series Data
Technical requirements
Understanding the time series dataset
pandas datetime operations, indexing, and slicing 
– a refresher
Handling missing data
Mapping additional information
Saving and loading files to disk
Handling longer periods of missing data
Summary
4
Chapter 3: Analyzing and Visualizing Time Series Data
Chapter 3: Analyzing and Visualizing Time Series Data
Technical requirements
Components of a time series
Visualizing time series data
Decomposing a time series
Detecting and treating outliers
Summary
References
Further reading
5
Chapter 4: Setting a Strong Baseline Forecast
Chapter 4: Setting a Strong Baseline Forecast
Technical requirements
Setting up a test harness
Generating strong baseline forecasts
Assessing the forecastability of a time series
Summary
References
Further reading
6
Part 2 – Machine Learning for Time Series
Part 2 – Machine Learning for Time Series
7
Chapter 5: Time Series Forecasting as Regression
Chapter 5: Time Series Forecasting as Regression
Understanding the basics of machine learning
Time series forecasting as regression
Global forecasting models – a paradigm shift
Summary
References
Further reading
8
Chapter 6: Feature Engineering for Time Series Forecasting
Chapter 6: Feature Engineering for Time Series Forecasting
Technical requirements
Feature engineering
Avoiding data leakage
Setting a forecast horizon
Time delay embedding
Temporal embedding
Summary
9
Chapter 7: Target Transformations for Time Series Forecasting
Chapter 7: Target Transformations for Time Series Forecasting
Technical requirements
Handling non-stationarity in time series
Detecting and correcting for unit roots
Detecting and correcting for trends
Detecting and correcting for seasonality
Detecting and correcting for heteroscedasticity
AutoML approach to target transformation
Summary
References
Further reading
10
Chapter 8: Forecasting Time Series with Machine Learning Models
Chapter 8: Forecasting Time Series with Machine Learning Models
Technical requirements
Training and predicting with machine learning models
Generating single-step forecast baselines
Standardized code to train and evaluate machine learning models
Training and predicting for multiple households
Summary
References
Further reading
11
Chapter 9: Ensembling and Stacking
Chapter 9: Ensembling and Stacking
Technical requirements
Combining forecasts
Stacking or blending
Summary
References
Further reading
12
Chapter 10: Global Forecasting Models
Chapter 10: Global Forecasting Models
Technical requirements
Why Global Forecasting Models (GFMs)?
Creating GFMs
Strategies to improve GFMs
Bonus – interpretability
Summary
References
Further reading
13
Part 3 – Deep Learning for Time Series
Part 3 – Deep Learning for Time Series
14
Chapter 11: Introduction to Deep Learning
Chapter 11: Introduction to Deep Learning
Technical requirements
What is deep learning and why now?
Components of a deep learning system
Summary
References
Further reading
15
Chapter 12: Building Blocks of Deep Learning for Time Series
Chapter 12: Building Blocks of Deep Learning for Time Series
Technical requirements
Understanding the encoder-decoder paradigm
Feed-forward networks
Recurrent neural networks
Long short-term memory (LSTM) networks
Gated recurrent unit (GRU)
Convolution networks
Summary
References
Further reading
16
Chapter 13: Common Modeling Patterns for Time Series
Chapter 13: Common Modeling Patterns for Time Series
Technical requirements
Tabular regression
Single-step-ahead recurrent neural networks
Sequence-to-sequence (Seq2Seq) models
Summary
Reference
Further reading
17
Chapter 14: Attention and Transformers for Time Series
Chapter 14: Attention and Transformers for Time Series
Technical requirements
What is attention?
The generalized attention model
Forecasting with sequence-to-sequence models and attention
Transformers – Attention is all you need
Forecasting with Transformers
Summary
References
Further reading
18
Chapter 15: Strategies for Global Deep Learning Forecasting Models
Chapter 15: Strategies for Global Deep Learning Forecasting Models
Technical requirements
Creating global deep learning forecasting models
Using time-varying information
Using static/meta information
Using the scale of the time series
Balancing the sampling procedure
Summary
Further reading
19
Chapter 16: Specialized Deep Learning Architectures for Forecasting
Chapter 16: Specialized Deep Learning Architectures for Forecasting
Technical requirements
The need for specialized architectures
Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)
Neural Basis Expansion Analysis for Interpretable Time Series Forecasting with Exogenous Variables (N-BEATSx)
Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)
Informer
Autoformer
Temporal Fusion Transformer (TFT)
Interpretability
Probabilistic forecasting
Summary
References
Further reading
20
Part 4 – Mechanics of Forecasting
Part 4 – Mechanics of Forecasting
21
Chapter 17: Multi-Step Forecasting
Chapter 17: Multi-Step Forecasting
Why multi-step forecasting?
Recursive strategy
Direct strategy
Joint strategy
Hybrid strategies
How to choose a multi-step forecasting strategy?
Summary
References
22
Chapter 18: Evaluating Forecasts – Forecast Metrics
Chapter 18: Evaluating Forecasts – Forecast Metrics
Technical requirements
Taxonomy of forecast error measures
Investigating the error measures
Experimental study of the error measures
Guidelines for choosing a metric
Summary
References
Further reading
23
Chapter 19: Evaluating Forecasts – Validation Strategies
Chapter 19: Evaluating Forecasts – Validation Strategies
Technical requirements
Model validation
Holdout strategies
Cross-validation strategies
Choosing a validation strategy
Validation strategies for datasets with multiple time series
Summary
References
Further reading
24
Index
Index
Why subscribe?
25
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Preface

Mankind has always sought the ability to predict the future. Since the earliest civilizations, people have tried to predict the future. Shamans, oracles, and prophets used anything ranging from astrology and palmistry to numerology to satisfy the human need to see into the future. In the last century, with the developments in IT, the mantle of predicting the future landed on data analysts and data scientists. And how do we predict the future? It’s not by examining the lines and creases on our hands or the positions of the stars anymore but by using data that has been generated in the past. And instead of prophecies, we now have forecasts.

Time, being the fourth dimension in our world, makes all the data generated in the world time series data. All the data that is generated in the real world has an element of time associated with it. Whether the temporal aspect is relevant to the problem or not is another question altogether. However, to be more concrete and immediate, we can find time series forecasting use cases in many industries, such as retail, energy, healthcare, and finance. We might want to know how many units of a particular product are to be dispatched to a particular store, or we might want to know how much electricity is to be produced to meet demand.

In this book, using a real-world dataset, you will learn how to handle and visualize time series data using pandas and plotly, generate baseline forecasts using darts, and use machine learning and deep learning for forecasting, using popular Python libraries such as scikit-learn and PyTorch. We conclude the book with a few chapters that cover seldom-touched aspects, such as multi-step forecasting, forecast metrics and cross validation for time series.

The book will enable you to build real-world time series forecasting systems that scale to millions of time series by mastering and applying modern concepts in machine learning and deep learning.

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