book

Machine Learning for Time-Series with Python

by Ben Auffarth

October 2021

Beginner to intermediate

370 pages

8h 19m

English

Packt Publishing

Read now

Unlock full access

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

11 Reinforcement Learning for Time-Series

Reinforcement learning is a widely successful paradigm for control problems and function optimization that doesn't require labeled data. It's a powerful framework for experience-driven autonomous learning, where an agent interacts directly with the environment by taking actions and improves its efficiency by trial and error. Reinforcement learning has been especially popular since the breakthrough of the London-based Google-owned company DeepMind in complex games.

In this chapter, we'll discuss a classification of reinforcement learning (RL) approaches in time-series specifically economics, and we'll deal with the accuracy and applicability of RL-based time-series models.

We'll start with core concepts ...