book

Machine Learning for Time-Series with Python

by Ben Auffarth

October 2021

Beginner to intermediate

370 pages

8h 19m

English

Packt Publishing

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Who this book is forWhat this book coversTo get the most out of this bookGet in touch
What Is a Time-Series?Characteristics of Time-SeriesTime-Series and Forecasting – Past and PresentDemographyGeneticsAstronomyEconomicsMeteorologyMedicineApplied StatisticsPython for Time-SeriesInstalling librariesJupyter Notebook and JupyterLabNumPypandasBest practice in PythonSummary
What is time-series analysis?Working with time-series in PythonRequirementsDatetimepandasUnderstanding the variablesUncovering relationships between variablesIdentifying trend and seasonalitySummary
What Is Preprocessing?Feature TransformsScalingLog and Power TransformationsImputationFeature EngineeringDate- and Time-Related FeaturesROCKETShapeletsPython PracticeLog and Power Transformations in PracticeImputationHoliday FeaturesDate AnnotationPaydaysSeasonsThe Sun and MoonBusiness DaysAutomated Feature ExtractionROCKETShapelets in PracticeSummary
Machine learning with time-seriesSupervised, unsupervised, and reinforcement learning History of machine learningMachine learning workflowCross-validation Error metrics for time-seriesRegressionClassificationComparing time-seriesMachine learning algorithms for time-seriesDistance-based approachesShapeletsROCKETTime-Series Forest and Canonical Interval ForestSymbolic approachesHIVE-COTEDiscussionImplementationsSummary
What are classical models?Moving average and autoregressionModel selection and orderExponential smoothingARCH and GARCHVector autoregressionPython librariesStatsmodelsPython practiceRequirementsModeling in PythonSummary
Unsupervised methods for time-seriesAnomaly detectionMicrosoftGoogleAmazonFacebookTwitterImplementationsChange point detectionClusteringPython practiceRequirementsAnomaly detectionChange point detectionSummary
More machine learning methods for time-seriesValidationK-nearest neighbors with dynamic time warpingSilverkiteGradient boostingPython exerciseVirtual environmentsK-nearest neighbors with dynamic time warping in PythonSilverkiteGradient boostingEnsembles with KatsSummary
Online learning for time-seriesOnline algorithmsDriftDrift detection methodsAdaptive learning methodsPython practiceDrift detectionRegressionModel selectionSummary
Probabilistic Models for Time-SeriesProphetMarkov ModelsFuzzy ModelingBayesian Structural Time-Series ModelsPython ExerciseProphetMarkov Switching ModelFuzzy Time-SeriesBayesian Structural Time-Series ModelingSummary

Introduction to deep learningDeep learning for time-seriesAutoencodersInceptionTimeDeepARN-BEATSRecurrent neural networksConvNetsTransformer architecturesInformerPython practiceFully connected networkRecurrent neural networkDilated causal convolutional neural networkSummary
Introduction to reinforcement learningReinforcement Learning for Time-SeriesBandit algorithmsDeep Q-LearningPython PracticeRecommendationsTrading with DQNSummary
Forecasting a Multivariate Time-SeriesPython practiceWhat's next for time-series?

Content preview from Machine Learning for Time-Series with Python

Preface

Time-series are ubiquitous in industry and in research. Examples of time-series can be found in healthcare, energy, finance, user behavior, and website metrics to name just a few. Due to their prevalence, time-series modeling and forecasting is crucial and it's of great economic importance to be able to model them accurately.

While traditional and well-established approaches have been dominating econometrics research and – until recently – industry, machine learning for time-series is a relatively new research field that's only recently come out of its infancy.

In the last few years, a lot of progress has been made in machine learning on time-series; however, little of this has been made available in book form for a technical audience. ...