Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning

Length: 552 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-11-24
ISBN-10: 1803246804
ISBN-13: 9781803246802
Sales Rank: #0 (See Top 100 Books)

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts

Key Features

Explore industry-tested machine learning techniques used to forecast millions of time series
Get started with the revolutionary paradigm of global forecasting models
Get to grips with new concepts by applying them to real-world datasets of energy forecasting

Book Description

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.

What you will learn

Find out how to manipulate and visualize time series data like a pro
Set strong baselines with popular models such as ARIMA
Discover how time series forecasting can be cast as regression
Engineer features for machine learning models for forecasting
Explore the exciting world of ensembling and stacking models
Get to grips with the global forecasting paradigm
Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer
Explore multi-step forecasting and cross-validation strategies

Who this book is for

The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

Modern Time Series Forecasting with Python
Contributors
About the author
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
        Setting up an environment
        Download the data
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
    Download a free PDF copy of this book
Part 1 – Getting Familiar with Time Series
Chapter 1: Introducing Time Series
    Technical requirements
    What is a time series?
        Types of time series
        Main areas of application for time series analysis
    Data-generating process (DGP)
        Generating synthetic time series
        Stationary and non-stationary time series
    What can we forecast?
    Forecasting terminology
    Summary
    Further reading
Chapter 2: 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
        Converting the date columns into pd.Timestamp/DatetimeIndex
        Using the .dt accessor and datetime properties
        Slicing and indexing
        Creating date sequences and managing date offsets
    Handling missing data
        Converting the half-hourly block-level data (hhblock) into time series data
        Compact, expanded, and wide forms of data
        Enforcing regular intervals in time series
        Converting the London Smart Meters dataset into a time series format
    Mapping additional information
    Saving and loading files to disk
    Handling longer periods of missing data
        Imputing with the previous day
        Hourly average profile
        The hourly average for each weekday
        Seasonal interpolation
    Summary
Chapter 3: Analyzing and Visualizing Time Series Data
    Technical requirements
    Components of a time series
        The trend component
        The seasonal component
        The cyclical component
        The irregular component
    Visualizing time series data
        Line charts
        Seasonal plots
        Seasonal box plots
        Calendar heatmaps
        Autocorrelation plot
    Decomposing a time series
        Detrending
        Deseasonalizing
        Implementations
    Detecting and treating outliers
        Standard deviation
        Interquartile range (IQR)
        Isolation Forest
        Extreme studentized deviate (ESD) and seasonal ESD (S-ESD)
        Treating outliers
    Summary
    References
    Further reading
Chapter 4: Setting a Strong Baseline Forecast
    Technical requirements
    Setting up a test harness
        Creating holdout (test) and validation datasets
        Choosing an evaluation metric
    Generating strong baseline forecasts
        Naïve forecast
        Moving average forecast
        Seasonal naive forecast
        Exponential smoothing (ETS)
        ARIMA
        Theta Forecast
        Fast Fourier Transform forecast
        Evaluating the baseline forecasts
    Assessing the forecastability of a time series
        Coefficient of Variation (CoV)
        Residual variability (RV)
        Entropy-based measures
        Kaboudan metric
    Summary
    References
    Further reading
Part 2 – Machine Learning for Time Series
Chapter 5: Time Series Forecasting as Regression
    Understanding the basics of machine learning
        Supervised machine learning tasks
        Overfitting and underfitting
        Hyperparameters and validation sets
    Time series forecasting as regression
        Time delay embedding
        Temporal embedding
    Global forecasting models – a paradigm shift
    Summary
    References
    Further reading
Chapter 6: Feature Engineering for Time Series Forecasting
    Technical requirements
    Feature engineering
    Avoiding data leakage
    Setting a forecast horizon
    Time delay embedding
        Lags or backshift
        Rolling window aggregations
        Seasonal rolling window aggregations
        Exponentially weighted moving averages (EWMA)
    Temporal embedding
        Calendar features
        Time elapsed
        Fourier terms
    Summary
Chapter 7: Target Transformations for Time Series Forecasting
    Technical requirements
    Handling non-stationarity in time series
    Detecting and correcting for unit roots
        Unit roots
        The Augmented Dickey-Fuller (ADF) test
        Differencing transform
    Detecting and correcting for trends
        Deterministic and stochastic trends
        Kendall’s Tau
        Mann-Kendall test (M-K test)
        Detrending transform
    Detecting and correcting for seasonality
        Detecting seasonality
        Deseasonalizing transform
    Detecting and correcting for heteroscedasticity
        Detecting heteroscedasticity
        Log transform
        Box-Cox transform
    AutoML approach to target transformation
    Summary
    References
    Further reading
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
        FeatureConfig
        MissingValueConfig
        ModelConfig
        MLForecast
        Helper functions for evaluating models
        Linear regression
        Regularized linear regression
        Decision trees
        Random forest
        Gradient boosting decision trees
    Training and predicting for multiple households
        Using AutoStationaryTransformer
    Summary
    References
    Further reading
Chapter 9: Ensembling and Stacking
    Technical requirements
    Combining forecasts
        Best fit
        Measures of central tendency
        Simple hill climbing
        Stochastic hill climbing
        Simulated annealing
        Optimal weighted ensemble
    Stacking or blending
    Summary
    References
    Further reading
Chapter 10: Global Forecasting Models
    Technical requirements
    Why Global Forecasting Models (GFMs)?
        Sample size
        Cross-learning
        Multi-task learning
        Engineering complexity
    Creating GFMs
    Strategies to improve GFMs
        Increasing memory
        Using time series meta-features
        Tuning hyperparameters
        Partitioning
    Bonus – interpretability
    Summary
    References
    Further reading
Part 3 – Deep Learning for Time Series
Chapter 11: Introduction to Deep Learning
    Technical requirements
    What is deep learning and why now?
        Why now?
        What is deep learning?
        Perceptron – the first neural network
    Components of a deep learning system
        Representation learning
        Linear transformation
        Activation functions
        Output activation functions
        Loss function
        Forward and backward propagation
    Summary
    References
    Further reading
Chapter 12: Building Blocks of Deep Learning for Time Series
    Technical requirements
    Understanding the encoder-decoder paradigm
    Feed-forward networks
    Recurrent neural networks
        The RNN layer in PyTorch
    Long short-term memory (LSTM) networks
        The LSTM layer in PyTorch
    Gated recurrent unit (GRU)
        The GRU layer in PyTorch
    Convolution networks
        Convolution
        Padding, stride, and dilations
        The convolution layer in PyTorch
    Summary
    References
    Further reading
Chapter 13: Common Modeling Patterns for Time Series
    Technical requirements
    Tabular regression
    Single-step-ahead recurrent neural networks
    Sequence-to-sequence (Seq2Seq) models
        RNN-to-fully connected network
        RNN-to-RNN
    Summary
    Reference
    Further reading
Chapter 14: Attention and Transformers for Time Series
    Technical requirements
    What is attention?
    The generalized attention model
        Alignment functions
        The distribution function
    Forecasting with sequence-to-sequence models and attention
    Transformers – Attention is all you need
        Attention is all you need
        Transformers in time series
    Forecasting with Transformers
    Summary
    References
    Further reading
Chapter 15: Strategies for Global Deep Learning Forecasting Models
    Technical requirements
    Creating global deep learning forecasting models
        Preprocessing the data
        Understanding TimeSeriesDataset from PyTorch Forecasting
        Building the first global deep learning forecasting model
    Using time-varying information
    Using static/meta information
        One-hot encoding and why it is not ideal
        Embedding vectors and dense representations
        Defining a model with categorical features
    Using the scale of the time series
    Balancing the sampling procedure
        Visualizing the data distribution
        Tweaking the sampling procedure
        Using and visualizing the dataloader with WeightedRandomSampler
    Summary
    Further reading
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)
        The architecture of N-BEATS
        Forecasting with N-BEATS
        Interpreting N-BEATS forecasting
    Neural Basis Expansion Analysis for Interpretable Time Series Forecasting with Exogenous Variables (N-BEATSx)
        Handling exogenous variables
        Exogenous blocks
    Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)
        The Architecture of N-HiTS
        Forecasting with N-HiTS
    Informer
        The architecture of the Informer model
        Forecasting with the Informer model
    Autoformer
        The architecture of the Autoformer model
        Forecasting with Autoformer
    Temporal Fusion Transformer (TFT)
        The Architecture of TFT
        Forecasting with TFT
        Interpreting TFT
    Interpretability
    Probabilistic forecasting
        Probability Density Function (PDF)
        Quantile functions
        Other approaches
    Summary
    References
    Further reading
Part 4 – Mechanics of Forecasting
Chapter 17: Multi-Step Forecasting
    Why multi-step forecasting?
    Recursive strategy
        Training regime
        Forecasting regime
    Direct strategy
        Training regime
        Forecasting regime
    Joint strategy
        Training regime
        Forecasting regime
    Hybrid strategies
        DirRec Strategy
        Iterative block-wise direct strategy
        Rectify strategy
        RecJoint
    How to choose a multi-step forecasting strategy?
    Summary
    References
Chapter 18: Evaluating Forecasts – Forecast Metrics
    Technical requirements
    Taxonomy of forecast error measures
        Intrinsic metrics
        Extrinsic metrics
    Investigating the error measures
        Loss curves and complementarity
        Bias towards over- or under-forecasting
    Experimental study of the error measures
        Using Spearman’s rank correlation
    Guidelines for choosing a metric
    Summary
    References
    Further reading
Chapter 19: Evaluating Forecasts – Validation Strategies
    Technical requirements
    Model validation
    Holdout strategies
        Window strategy
        Calibration strategy
        Sampling strategy
    Cross-validation strategies
    Choosing a validation strategy
    Validation strategies for datasets with multiple time series
    Summary
    References
    Further reading
Index
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