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Machine Learning for Time Series with Python - Second Edition

Name: Machine Learning for Time Series with Python - Second Edition
Author: Ben Auffarth
ISBN: 9781837631339

by Ben Auffarth

September 2026

Intermediate to advanced

431 pages

5h 58m

English

Packt Publishing

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Welcome to Packt Early Access
Machine Learning for Time Series with Python, Second Edition: Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Chapter 1: Towards Modern Forecasting
Join our book community on DiscordThe evolution of forecasting methodsWhat is a time series?Concrete examples: time series in the python contextWhen do smart models fail?Why algorithms aren't enoughWhy advanced models still failFailure mode 1: misleading validation metricsFailure mode 2: integration breakdownFailure mode 3: inability to adapt to changeThe three fundamental gapsGap 1: temporal assumption violationsGap 2: misalignment with business needsGap 3: the problem with fixed modelsBuilding adaptive systemsQuestion 1: is this problem actually foreseeable?Question 2: what decisions will this prediction enable?Question 3: how will patterns change over time?Question 4: how quickly can I adapt when I'm wrong?Why choose python?Choosing the right tools for time series problemsForecastingClassificationAnomaly detectionRegressionCore principles for production-ready systemsCommon pitfalls and how to avoid themPattern 1: static system thinking to dynamic system designPattern 2: optimization mindset to adaptation infrastructurePattern 3: point estimate fixation to uncertainty-aware decision-makingPattern 4: independence assumptions to temporal structure respectPattern 5: technical metric obsession to business value alignmentPattern 6: model artifact focus to production system designSummaryResources
Chapter 2: Preparing and Visualizing Time Series Data
Join our book community on DiscordTechnical requirementsMathematical foundations for data understandingTemporal dependencies and autocorrelationProving temporal structure existsInterpreting autocorrelation patterns for modeling strategiesTools for autocorrelation analysisStationarity testing and interpretationVisualizing stationarityStatistical stationarity testsAchieving stationarityStabilizing variance with log and Box–Cox transformationsSignals from decompositionWhy the additive vs.multiplicative choice mattersPractical decomposition in pythonChoosing the right decomposition methodBuilding a time series data pipelineTemporal indexing and frequency managementThe strategic solution: robust conversion and validationQuality checks and missing dataMissing data assessmentDetecting structural breaksQuality threshold decision-makingFrequency conversion and resampling decisionsThe resampling trap: Look-Ahead biasPractical decision frameworkPattern discovery through visualizationVisual diagnostics for temporal structuresAutocorrelation and residual analysisForecastability assessmentMeasuring predictability before modelingSignal-to-noise proxyThe naive modelThe seasonal naive benchmarkA framework for project viabilitySummary
Chapter 3: Classical Models and Validation
Join our book community on DiscordWhy statistical methods matterThe role of baselines in forecastingClassical baselinesExponential smoothingARIMATheta MethodOverview of statistical forecasting methodsCase study: forecasting "remote work"Validation and metricsWhy random splits failExpanding windowRolling windowAdvanced techniquesWalk-forward validationNested cross-validationPerformance metricsScale-dependent metricsScale- independent metricsAn experimentIndustry-specific validation strategiesSummary
Chapter 4: Forecasting with Machine Learning
Join our book community on DiscordForecasting as a regression taskThe ML forecasting workflowStep 1: Data preparationStep 2: Feature engineeringStep 3: Model trainingStep 4: PredictionStep 5: ValidationThe role of toolingTools for the jobSktimeDartsThe Nixtla ecosystemAutoML Platforms (autogluon)Direct scikit-learn integration (skforecast)Choosing your tool: a decision frameworkCreating an ML forecastStep 1: Setup and baselineStep 2: Manual Feature EngineeringStep 3: Training the modelStep 4: PredictionStep 5: ValidationSummary
Chapter 5: Feature Engineering and Tree-Based Models
Join our book community on DiscordTechnical requirementsFeature engineeringCalendar featuresEvent and holiday featuresAdvanced feature engineeringLag featuresRolling window statisticsEncoding cyclical timeDomain-specific featuresA practical guide to feature engineeringBuilding a reusable pipelineChoosing a tree modelHybrid modeling and decompositionPattern 1: Decompose-then-model-residualsPattern 2: Fourier features for seasonalityAutomated feature engineeringRecipe 1: mlforecast and XGBoostRecipe 2: tsfresh and DartsRecipe 3: functimeSummary
Chapter 6: Multivariate and Hierarchical Forecasting
Join our book community on DiscordMultivariate modelsWhat are multivariate forecasts?VAR and VARMAXA simple VAR modelGranger causalityVARMAXGlobal multi-series modelsHierarchical modelsWhat are hierarchies?Reconciliation approachesThe bottom-up approachThe top-down approachThe middle-out approachModern reconciliation: optimal combinationsCase study: Forecasting tourism in AustraliaEvaluating and comparing resultsSummaryFurther reading

Content preview from Machine Learning for Time Series with Python - Second Edition

Welcome to Packt Early Access

Machine Learning for Time Series with Python, Second Edition: Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods

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Machine Learning for Time Series Forecasting with Python

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Machine Learning for Time Series with Python - Second Edition

by Ben Auffarth

Welcome to Packt Early Access

Machine Learning for Time Series with Python, Second Edition: Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.