book

Practical Time Series Analysis

by Aileen Nielsen

October 2019

Beginner to intermediate

497 pages

14h

English

O'Reilly Media, Inc.

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Who Should Read This BookExpected BackgroundWhy I Wrote This BookNavigating This BookOnline ResourcesConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
The History of Time Series in Diverse ApplicationsMedicine as a Time Series ProblemForecasting WeatherForecasting Economic GrowthAstronomyTime Series Analysis Takes OffThe Origins of Statistical Time Series AnalysisThe Origins of Machine Learning Time Series AnalysisMore Resources
Where to Find Time Series DataPrepared Data SetsFound Time SeriesRetrofitting a Time Series Data Collection from a Collection of TablesA Worked Example: Assembling a Time Series Data CollectionConstructing a Found Time SeriesTimestamping TroublesWhose Timestamp?Guesstimating Timestamps to Make Sense of DataWhat’s a Meaningful Time Scale?Cleaning Your DataHandling Missing DataUpsampling and DownsamplingSmoothing DataSeasonal DataTime ZonesPreventing LookaheadMore Resources
Familiar MethodsPlottingHistogramsScatter PlotsTime Series–Specific Exploratory MethodsUnderstanding StationarityApplying Window FunctionsUnderstanding and Identifying Self-CorrelationSpurious CorrelationsSome Useful Visualizations1D Visualizations2D Visualizations3D VisualizationsMore Resources
What’s Special About Simulating Time Series?Simulation Versus ForecastingSimulations in CodeDoing the Work YourselfBuilding a Simulation Universe That Runs ItselfA Physics SimulationFinal Notes on SimulationsStatistical SimulationsDeep Learning SimulationsMore Resources
Defining RequirementsLive Data Versus Stored DataDatabase SolutionsSQL Versus NoSQLPopular Time Series Database and File SolutionsFile SolutionsNumPyPandasStandard R EquivalentsXarrayMore Resources
Why Not Use a Linear Regression?Statistical Methods Developed for Time SeriesAutoregressive ModelsMoving Average ModelsAutoregressive Integrated Moving Average ModelsVector AutoregressionVariations on Statistical ModelsAdvantages and Disadvantages of Statistical Methods for Time SeriesMore Resources
State Space Models: Pluses and MinusesThe Kalman FilterOverviewCode for the Kalman FilterHidden Markov ModelsHow the Model WorksHow We Fit the ModelFitting an HMM in CodeBayesian Structural Time SeriesCode for bstsMore Resources
Introductory ExampleGeneral Considerations When Computing FeaturesThe Nature of the Time SeriesDomain KnowledgeExternal ConsiderationsA Catalog of Places to Find Features for InspirationOpen Source Time Series Feature Generation LibrariesDomain-Specific Feature ExamplesHow to Select Features Once You Have Generated ThemConcluding ThoughtsMore Resources
Time Series ClassificationSelecting and Generating FeaturesDecision Tree MethodsClusteringGenerating Features from the DataTemporally Aware Distance MetricsClustering CodeMore Resources

Deep Learning ConceptsProgramming a Neural NetworkData, Symbols, Operations, Layers, and GraphsBuilding a Training PipelineInspecting Our Data SetSteps of a Training PipelineFeed Forward NetworksA Simple ExampleUsing an Attention Mechanism to Make Feed Forward Networks More Time-AwareCNNsA Simple Convolutional ModelAlternative Convolutional ModelsRNNsContinuing Our Electric ExampleThe Autoencoder InnovationCombination ArchitecturesSumming UpMore Resources
The Basics: How to Test ForecastsModel-Specific Considerations for BacktestingWhen Is Your Forecast Good Enough?Estimating Uncertainty in Your Model with a SimulationPredicting Multiple Steps AheadFit Directly to the Horizon of InterestRecursive Approach to Distant Temporal HorizonsMultitask Learning Applied to Time SeriesModel Validation GotchasMore Resources
Working with Tools Built for More General Use CasesModels Built for Cross-Sectional Data Don’t “Share” Data Across SamplesModels That Don’t Precompute Create Unnecessary Lag Between Measuring Data and Making a ForecastData Storage Formats: Pluses and MinusesStore Your Data in a Binary FormatPreprocess Your Data in a Way That Allows You to “Slide” Over ItModifying Your Analysis to Suit Performance ConsiderationsUsing All Your Data Is Not Necessarily BetterComplicated Models Don’t Always Do Better EnoughA Brief Mention of Alternative High-Performance ToolsMore Resources
Predicting the FluA Case Study of Flu in One Metropolitan AreaWhat Is State of the Art in Flu Forecasting?Predicting Blood Glucose LevelsData Cleaning and ExplorationGenerating FeaturesFitting a ModelMore Resources
Obtaining and Exploring Financial DataPreprocessing Financial Data for Deep LearningAdding Quantities of Interest to Our Raw ValuesScaling Quantities of Interest Without a LookaheadFormatting Our Data for a Neural NetworkBuilding and Training an RNNMore Resources
Obtaining Governmental DataExploring Big Time Series DataUpsample and Aggregate the Data as We Iterate Through ItSort the DataOnline Statistical Analysis of Time Series DataRemaining QuestionsFurther ImprovementsMore Resources
Forecasting at ScaleGoogle’s Industrial In-house ForecastingFacebook’s Open Source Prophet PackageAnomaly DetectionTwitter’s Open Source AnomalyDetection PackageOther Time Series PackagesMore Resources
Forecasting as a ServiceDeep Learning Enhances Probabilistic PossibilitiesIncreasing Importance of Machine Learning Rather Than StatisticsIncreasing Combination of Statistical and Machine Learning MethodologiesMore Forecasts for Everyday Life

Content preview from Practical Time Series Analysis

Preface

Weather, stock markets, and heartbeats. They all form time series. If you’re interested in diverse data and forecasting the future, you’re interested in time series analysis.

Welcome to Practical Time Series Analysis! If you picked this book up, you’ve probably already noticed that time series data is everywhere. Time series data grows increasingly ubiquitous and important as the big data ecosystem expands. For better or worse, sensors and tracking mechanisms are everywhere, and as a result there are unprecedented amounts of high-quality time series data available. Time series are uniquely interesting because they can address questions of causality, trends, and the likelihood of future outcomes. This book will take you through the major techniques that are most commonly applied to time series to address such questions.

Time series data spans a wide range of disciplines and use cases. It can be anything from customer purchase histories to conductance measurements of a nano-electronic system to digital recordings of human language. One point we discuss throughout the book is that time series analysis applies to a surprisingly diverse set of data. Any data that has an ordered axis can be analyzed with time series methods, even if that ordered axis is not time per se. Traditional time series data, such as stock data and weather patterns, can be analyzed with time series methods, but so can quirky data sets such as spectrographs of wine, where the “time” axis is actually an ...