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Home > Data > Data Science > Modern Time Series Forecasting with Python
Modern Time Series Forecasting with Python
Modern Time Series Forecasting with Python

Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas , Second Edition

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Modern Time Series Forecasting with Python

Introducing Time Series

Welcome to Modern Time Series Forecasting with Python! This book is intended for data scientists or machine learning (ML) engineers who want to level up their time series analysis skills by learning new and advanced techniques from the ML world. Time series analysis is something that is commonly overlooked in regular ML books, courses, and so on. They typically start with classification, touch upon regression, and then move on. But it is also something that is immensely valuable and ubiquitous in business. We look at the world from a three-dimensional perspective. Time is the hidden dimension that we rarely think about, but is all-pervasive. And as long as time is one of the four dimensions in the world we live in, time series data is all-pervasive too.

Analyzing time series data unlocks a lot of value for a business. Time series analysis isn’t new—it’s been around since the 1920s. But in the current age of data, the time series that...

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https://packt.link/EarlyAccess/

Welcome to Modern Time Series Forecasting with Python! This book is intended for data scientists or machine learning (ML) engineers who want to level up their time series analysis skills by learning new and advanced techniques from the ML world. Time series analysis is something that is commonly overlooked in regular ML books, courses, and so on. They typically start with classification, touch upon regression, and then move on. But it is also something that is immensely valuable and ubiquitous in business. We look at the world in a three-dimensional perspective. But time is the hidden dimension which we rarely think about, but is all-pervasive. And as long as time is one of the four dimensions in the world we live in, time series data is all-pervasive.

Analyzing time series data unlocks a lot of value for a business. Time series analysis isn't new—it's been around since the 1920s. But in the current...

Technical requirements

You will need to set up the Anaconda environment following the instructions in the Preface of the book to get a working environment with all the libraries and datasets required for the code in this book. Any additional library will be installed while running the notebooks.

The associated code for the chapter can be found at https://github.com/PacktPublishing/Modern-Time-Series-Forecasting-with-Python-/tree/main/notebooks/Chapter01.

What is a time series?

To keep it simple, a time series is a set of observations taken sequentially in time. The focus is on the word time. If we keep taking the same observation at different points in time, we will get a time series. For example, if you keep recording the number of bars of chocolate you have in a month, you'll end up with a time series of your chocolate consumption. Suppose you are recording your weight at the beginning of every month. You get another time series of your weight. Is there any relation between the two time series? Most likely, yeah. But we can analyze that scientifically by the end of this book.

A few other examples of time series are the weekly closing price of a stock that you follow, daily rainfall or snow in your city, or hourly readings of your heartbeat from your smartwatch.

Types of time series

There are two types of time series data based on time-intervals, as outlined here:

Regular time series: This is the most common type of time series...

Data-generating process (DGP)

We saw that time series data is a collection of observations made sequentially along the time dimension. Any time series is, in turn, generated by some kind of mechanism. For example, time series data of daily shipments of your favorite chocolate from the manufacturing plant is affected by a lot of factors such as the time of the year, the holiday season, the availability of cocoa, the uptime of the machines working on the plant, and so on. In statistics, this underlying process that generated the time series is referred to as the DGP. Time series data is produced by stochastic and deterministic process. The deterministic processes involve quantities that evolve in a predictable manner over time. An example of this is the radioactive decay of an element, where the remaining quantity diminishes according to a precise mathematical formula, leading to a consistent reduction over time. But most of the interesting time series (from a forecasting perspective) are...

What can we forecast?

Before we move ahead, there is another aspect of time series forecasting that we have to understand—the predictability of a time series. The most basic assumption when we forecast a time series is that the future depends on the past. But not all time series are equally predictable.

Let's take a look at a few examples and try to rank these in order of predictability (from easiest to hardest), as follows:

  • High tide next Monday
  • Lottery numbers next Sunday
  • The stock price of Tesla next Friday

Intuitively, it is very easy for us to rank them. High tide next Monday is going to be the easiest to predict because it is so predictable, the lottery numbers are going to be very hard to predict because these are pretty much random, and the stock price of Tesla next Friday is going to be difficult to predict, but not impossible.

NOTE

However, for people thinking that they can forecast stock prices with the advanced techniques covered in the book and get rich, that...

Forecasting terminology

There are a few terminologies that will help you follow the book as well as other literature on time series. These are described in more detail here:

  • Forecasting

Forecasting is the prediction of future values of a time series using the known past values of the time series and/or some other related variables. This is very similar to prediction in ML where we use a model to predict unseen data.

  • Multivariate forecasting

Multivariate time series consist of more than one time series variable that is not only dependent on its past values but also has some dependency on the other variables. For example, a set of macroeconomic indicators such as gross domestic product (GDP), inflation, and so on of a particular country can be considered as a multivariate time series. The aim of multivariate forecasting is to come up with a model that captures the interrelationship between the different variables along with its relationship with its past and forecast all the time series...

Summary

We had our first dip into time series as we understood the different types of time series, looked at how a DGP generates a time series, and saw how we can think about the important question: How well can we forecast a time series? We also had a quick review of the terminology and notation required to understand the rest of the book. In the next chapter, we will be getting our hands dirty and will learn how to work with time series data, how to preprocess a time series, how to handle missing data and outliers, and so on. If you have not set up the environment yet, take a break and put some time into doing that.

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Key benefits

  • Apply ML and global models to improve forecasting accuracy through practical examples
  • Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS
  • Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions
  • Purchase of the print or Kindle book includes a free eBook in PDF format

Description

Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.

Who is this book for?

This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.

What you will learn

  • Build machine learning models for regression-based time series forecasting
  • Apply powerful feature engineering techniques to enhance prediction accuracy
  • Tackle common challenges like non-stationarity and seasonality
  • Combine multiple forecasts using ensembling and stacking for superior results
  • Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series
  • Evaluate and validate your forecasts using best practices and statistical metrics
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Length: 658 pages
Edition : 2nd
Language : English
ISBN-13 : 9781835883181
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Table of Contents

25 Chapters
Part-1: Getting Familiar with Time Series Chevron down icon Chevron up icon
Part-1: Getting Familiar with Time Series
Introducing Time Series Chevron down icon Chevron up icon
Introducing Time Series
Technical requirements
What is a time series?
Data-generating process (DGP)
What can we forecast?
Forecasting terminology
Summary
Further reading
Acquiring and Processing Time Series Data Chevron down icon Chevron up icon
Acquiring and Processing Time Series Data
Technical requirements
Understanding the time series dataset
Preparing a data model
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
Analyzing and Visualizing Time Series Data Chevron down icon Chevron up icon
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
Setting a Strong Baseline Forecast Chevron down icon Chevron up icon
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
Part-2: Machine Learning for Time Series Chevron down icon Chevron up icon
Part-2: Machine Learning for Time Series
Time Series Forecasting as Regression Chevron down icon Chevron up icon
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
Feature Engineering for Time Series Forecasting Chevron down icon Chevron up icon
Feature Engineering for Time Series Forecasting
Technical requirements
Understanding feature engineering
Avoiding data leakage
Setting a forecast horizon
Time delay embedding
Temporal embedding
Summary
Target Transformations for Time Series Forecasting Chevron down icon Chevron up icon
Target Transformations for Time Series Forecasting
Technical requirements
Detecting 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
Forecasting Time Series with Machine Learning Models Chevron down icon Chevron up icon
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
Ensembling and Stacking Chevron down icon Chevron up icon
Ensembling and Stacking
Technical requirements
Combining forecasts
Stacking and blending
Summary
References
Further reading
Global Forecasting Models Chevron down icon Chevron up icon
Global Forecasting Models
Technical requirements
Why Global Forecasting Models?
Creating GFMs
Strategies to improve GFMs
Interpretability
Summary
References
Further reading
Part-3: Deep Learning for Time Series Chevron down icon Chevron up icon
Part-3: Deep Learning for Time Series
Introduction to Deep Learning Chevron down icon Chevron up icon
Introduction to Deep Learning
Technical requirements
What is deep learning and why now?
Components of a deep learning system
Summary
References
Further reading
Building Blocks of Deep Learning for Time Series Chevron down icon Chevron up icon
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
Common Modeling Patterns for Time Series Chevron down icon Chevron up icon
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
Attention and Transformers for Time Series Chevron down icon Chevron up icon
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
Strategies for Global Deep Learning Forecasting Models Chevron down icon Chevron up icon
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
Specialized Deep Learning Architectures for Forecasting Chevron down icon Chevron up icon
Specialized Deep Learning Architectures for Forecasting
Technical requirements
The need for specialized architectures
Introduction to NeuralForecast
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)
Autoformer
LTSF-Linear family of models
Patch Time Series Transformer (PatchTST)
iTransformer
Temporal Fusion Transformer (TFT)
TSMixer
Time Series Dense Encoder (TiDE)
Summary
References
Further reading
Probabilistic Forecasting and More Chevron down icon Chevron up icon
Probabilistic Forecasting and More
Probabilistic forecasting
Road less traveled in time series forecasting
Summary
References
Further reading
Part-4: Mechanics of Forecasting Chevron down icon Chevron up icon
Part-4: Mechanics of Forecasting
Multi-Step Forecasting Chevron down icon Chevron up icon
Multi-Step Forecasting
Why multi-step forecasting?
Standard notation
Recursive strategy
Direct strategy
The Joint strategy
Hybrid strategies
How to choose a multi-step forecasting strategy
Summary
References
Evaluating Forecast Errors—A Survey of Forecast Metrics Chevron down icon Chevron up icon
Evaluating Forecast Errors—A Survey of 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
Evaluating Forecasts—Validation Strategies Chevron down icon Chevron up icon
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
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About the authors

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Profile icon Manu Joseph
Manu Joseph
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Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son
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Profile icon Jeffrey Tackes
Jeffrey Tackes
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Jeff Tackes is a seasoned data scientist specializing in demand forecasting with over a decade of industry experience. Currently he is at Kraft Heinz, where he leads the research team in charge of demand forecasting. He has pioneered the development of best-in-class forecasting systems utilized by leading Fortune 500 companies. Jeff's approach combines a robust data-driven methodology with innovative strategies, enhancing forecasting models and business outcomes significantly. Leading cross-functional teams, Jeff has designed and implemented demand forecasting systems that have markedly improved forecast accuracy, inventory optimization, and customer satisfaction. His proficiency in statistical modeling, machine learning, and advanced analytics has led to the implementation of forecasting methodologies that consistently surpass industry norms. Jeff's strategic foresight and his capability to align forecasting initiatives with overarching business objectives have established him as a trusted advisor to senior executives and a prominent expert in the data science domain. Additionally, Jeff actively contributes to the open-source community, notably to PyTimeTK, where he develops tools that enhance time series analysis capabilities. He currently resides in Chicago, IL with his wife and son.
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Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela