Preface 1．TimeSeries：AnOverviewand aQuickHistory The History of Time Series in Diverse Applications Medicine as a Time Series Problem Forecasting Weather Forecasting Economic Growth Astronomy Time Series Analysis Takes Off The Origins of Statistical Time Series Analysis The Origins of Machine Learning Time Series Analysis More Resources 2．FindingandWranglingTimeSeriesData where to Find Time Series Data Prepared Data Sets Found Time Series Retrofitting a Time Series Data Collection from a Collection of Tables A Worked Example：Assembling a Time Series Data Collection Constructing a Found Time Series Timestamping Troubles Whose Timestamp Guesstimating Timestamps to Make Sense of Data What』s a Meaningful Time Scale Cleaning Your Data Handling Missing Data Upsampling and Downsampling Smoothing Data Seasonal Data Time Zones Preventing Lookahead More Resources 3．ExploratoryDataAnalysisforTimeSeries Familiar Methods Plotting Histograms Scatter Plots Time Series-Specific Exploratory Methods Understanding Stationarity Applying Window Functions Understanding and Identifying Self-Correlation Spurious Correlations Some Useful Visualizations lD Visualizations 2D Visualizations 3D Visualizations More Resources 4．SimulatingTimeSeriesData What』S Special About Simulating Time Series Simulation Versus Forecasting Simulations in Code Doing the Work Yourself
Building a Simulation Universe That Runs Itself A Physics Simulation Final Notes on Simulations Statistical Simulations Deep Learning Simulations More Resources 5．StoringTemporalData Defining Requirements Live Data Versus Stored Data Database Solutions SQL Versus NoSQL Popular Time Series Database and File Solutions File Solutions NumPv Pandas Standard R Equivalents Xarray More Resources 6．StatisticaIModelsforTimeSeries Why Not Use a Linear Regression Statistical Methods Developed for Time Series Autoregressive Models Moving Average Models Autoregressive Integrated Moving Average Models Vector Autoregression Variations on Statistical Models Advantages and Disadvantages of Statistical Methods for Time Series More Resources 7．StateSpaceModels for TimeSeries State Space Models：Pluses and Minuses The Kalman Filter Overview CodefortheKalmanFilter、 Hidden Markov Modds HOW the Model Works HOWWeFittheModel Fitting an HMM in Code Bayesian Structural Time Series Code forbsts More Resources 8．Generating and Selecting FeaturesforaTimeSeries Introductory Example General Considerations When Computing Features The Nature of the Time Series Domain Knowledge External Considerations A Catalog of Places to Find Features for Inspiration Open Source Time Series Feature Generation Libraries Domain-Specific Feature Examples How to Select Features 0nce You Have Generated Them
Concluding Thoughts More Resources 9．Machine LearningforTime Series Time Series C：lassification Selecting and Generating Features Decision Tree Methods Clustering Generating Features from the Data TemporaUy Aware Distance Metrics Clustering Code More Resources 10．Deep LearningforTimeSeries Deep Learning Concepts Programming a Neural Network Data，Symbols，Operations，Layers，and Graphs Building a Training Pipeline Inspecting Our Data Set Steps of a Training Pipeline Feed Forward Networks A Simple Example Using an Attention Mechanism to Make Feed Forward Networks More Time—Aware CNNS A Simple Convolutional Model Alternative Convolutional Models RNNS Continuing Our Electric Example The Autoencoder Innovation Combination Architectures Summing Up More Resources 11．Measuring Error The Basics：HoW to Test Forecasts Model-Specific Considerations for Backtesting When Is Your Forecast Good Enough Estimating Uncertainty in Your Model with a Simulation Predicting Multiple Steps Ahead Fit Directlv to the Horizon of Interest Recursive Approach to Distant Temporal Horizons Multitask Learning Applied to Time Series Model Validation Gotchas More Resources 1 2．Performance Considerations in Fitting and Serving Time Series Models Working with Tools Built for More General Use Cases Models Built for Cross．Sectional Data Don't Share」Data Across Samples Models That Don』t Precompute Create Unnecessary Lag Between Measuring Data and Making a Forecast Data Storage Formats：Pluses and Minuses Store Your Data in a Binary Format Preprocess Your Data in a Way That Allows Yon to「Slide」Over It
Modi研ng Your Analysis to Suit Performance Considerations Using A11 Your Data Is Not Necessarily Better Complicated Models Don』t Always Do Better Enough A Brief Mention of Alternative High—Performance Tools More Resources 13．HealthcareApplications Predicting the Flu A Case Study of Flu in 0ne Metropolitan Area What Is State of the Art in Flu Forecasting Predicting Blood Glucose Levels Data Cleaning and Exploration Generating Features Fitting a Model More Resources 14．FinanciaIApplications Obtaining and Exploring Financial Data Preprocessing Financial Data for Deep Learning Adding Quantities of Interest to Our Raw Values Scaling Quantities of Interest Without a Lookahead Formatting 0ur Data for a Neural Network Building and Training an RNN More Resources 15．TimeSeriesforGovernment Obtaining Governmental Data Exploring Big Time Series Data Upsample and Aggregate the Data as We Iterate Through It Sort the Data 0nline Statistical Analysis of Time Series Data Remaining Questions Further Improvements More Resources 16．TimeSeriesPackages Forecasting at Scale Google』S Industrial In．house Forecasting Facebook』S Open Source Prophet Package Anomaly Detection Twitter』s Open Source AnomalyDetection Package Other Time Series Packages More Resources 17．ForecastsAbout Forecasting Forecasting as a Service Deep Learning Enhances Probabilistic Possibilities Increasing Importance of Machine Learning Rather Than Statistics Increasing Combination of Statistical and Machine Learning Methodologies More Forecasts for Everyday Life Index
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