book

Modern Time Series Forecasting with Python

Name: Modern Time Series Forecasting with Python
Author: Manu Joseph
ISBN: 9781803246802

by Manu Joseph

November 2022

Beginner to intermediate

552 pages

16h 4m

English

Packt Publishing

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Modern Time Series Forecasting with Python
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookSetting up an environmentDownload the dataDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1 – Getting Familiar with Time Series
Chapter 1: Introducing Time Series
Technical requirementsWhat is a time series?Types of time seriesMain areas of application for time series analysisData-generating process (DGP)Generating synthetic time seriesStationary and non-stationary time seriesWhat can we forecast?Forecasting terminologySummaryFurther reading
Chapter 2: Acquiring and Processing Time Series Data
Technical requirementsUnderstanding the time series datasetPreparing a data modelpandas datetime operations, indexing, and slicing  – a refresherConverting the date columns into pd.Timestamp/DatetimeIndexUsing the .dt accessor and datetime propertiesSlicing and indexingCreating date sequences and managing date offsetsHandling missing dataConverting the half-hourly block-level data (hhblock) into time series dataCompact, expanded, and wide forms of dataEnforcing regular intervals in time seriesConverting the London Smart Meters dataset into a time series formatMapping additional informationSaving and loading files to diskHandling longer periods of missing dataImputing with the previous dayHourly average profileThe hourly average for each weekdaySeasonal interpolationSummary
Chapter 3: Analyzing and Visualizing Time Series Data
Technical requirementsComponents of a time seriesThe trend componentThe seasonal componentThe cyclical componentThe irregular componentVisualizing time series dataLine chartsSeasonal plotsSeasonal box plotsCalendar heatmapsAutocorrelation plotDecomposing a time seriesDetrendingDeseasonalizingImplementationsDetecting and treating outliersStandard deviationInterquartile range (IQR)Isolation ForestExtreme studentized deviate (ESD) and seasonal ESD (S-ESD)Treating outliersSummaryReferencesFurther reading
Chapter 4: Setting a Strong Baseline Forecast
Technical requirementsSetting up a test harnessCreating holdout (test) and validation datasetsChoosing an evaluation metricGenerating strong baseline forecastsNaïve forecastMoving average forecastSeasonal naive forecastExponential smoothing (ETS)ARIMATheta ForecastFast Fourier Transform forecastEvaluating the baseline forecastsAssessing the forecastability of a time seriesCoefficient of Variation (CoV)Residual variability (RV)Entropy-based measuresKaboudan metricSummaryReferencesFurther reading
Part 2 – Machine Learning for Time Series
Chapter 5: Time Series Forecasting as Regression
Understanding the basics of machine learningSupervised machine learning tasksOverfitting and underfittingHyperparameters and validation sets Time series forecasting as regressionTime delay embeddingTemporal embeddingGlobal forecasting models – a paradigm shiftSummaryReferencesFurther reading
Chapter 6: Feature Engineering for Time Series Forecasting
Technical requirementsFeature engineeringAvoiding data leakageSetting a forecast horizonTime delay embeddingLags or backshiftRolling window aggregationsSeasonal rolling window aggregationsExponentially weighted moving averages (EWMA)Temporal embeddingCalendar featuresTime elapsedFourier termsSummary

Chapter 7: Target Transformations for Time Series Forecasting
Technical requirementsHandling non-stationarity in time seriesDetecting and correcting for unit rootsUnit rootsThe Augmented Dickey-Fuller (ADF) testDifferencing transformDetecting and correcting for trendsDeterministic and stochastic trendsKendall’s TauMann-Kendall test (M-K test)Detrending transformDetecting and correcting for seasonalityDetecting seasonalityDeseasonalizing transformDetecting and correcting for heteroscedasticityDetecting heteroscedasticityLog transformBox-Cox transformAutoML approach to target transformationSummaryReferencesFurther reading
Chapter 8: Forecasting Time Series with Machine Learning Models
Technical requirementsTraining and predicting with machine learning modelsGenerating single-step forecast baselinesStandardized code to train and evaluate machine learning modelsFeatureConfigMissingValueConfigModelConfigMLForecastHelper functions for evaluating modelsLinear regressionRegularized linear regressionDecision treesRandom forestGradient boosting decision treesTraining and predicting for multiple householdsUsing AutoStationaryTransformerSummaryReferencesFurther reading
Chapter 9: Ensembling and Stacking
Technical requirementsCombining forecastsBest fitMeasures of central tendencySimple hill climbingStochastic hill climbingSimulated annealingOptimal weighted ensembleStacking or blendingSummaryReferencesFurther reading
Chapter 10: Global Forecasting Models
Technical requirementsWhy Global Forecasting Models (GFMs)?Sample sizeCross-learningMulti-task learningEngineering complexityCreating GFMsStrategies to improve GFMsIncreasing memoryUsing time series meta-featuresTuning hyperparametersPartitioningBonus – interpretabilitySummaryReferencesFurther reading
Part 3 – Deep Learning for Time Series
Chapter 11: Introduction to Deep Learning
Technical requirementsWhat is deep learning and why now?Why now?What is deep learning?Perceptron – the first neural networkComponents of a deep learning systemRepresentation learningLinear transformationActivation functionsOutput activation functionsLoss functionForward and backward propagationSummaryReferencesFurther reading
Chapter 12: Building Blocks of Deep Learning for Time Series
Technical requirementsUnderstanding the encoder-decoder paradigmFeed-forward networksRecurrent neural networksThe RNN layer in PyTorchLong short-term memory (LSTM) networksThe LSTM layer in PyTorchGated recurrent unit (GRU)The GRU layer in PyTorchConvolution networksConvolutionPadding, stride, and dilationsThe convolution layer in PyTorchSummaryReferencesFurther reading
Chapter 13: Common Modeling Patterns for Time Series
Technical requirementsTabular regressionSingle-step-ahead recurrent neural networksSequence-to-sequence (Seq2Seq) modelsRNN-to-fully connected networkRNN-to-RNNSummaryReferenceFurther reading
Chapter 14: Attention and Transformers for Time Series
Technical requirementsWhat is attention?The generalized attention modelAlignment functionsThe distribution functionForecasting with sequence-to-sequence models and attentionTransformers – Attention is all you needAttention is all you needTransformers in time seriesForecasting with TransformersSummaryReferencesFurther reading
Chapter 15: Strategies for Global Deep Learning Forecasting Models
Technical requirementsCreating global deep learning forecasting modelsPreprocessing the dataUnderstanding TimeSeriesDataset from PyTorch ForecastingBuilding the first global deep learning forecasting modelUsing time-varying informationUsing static/meta informationOne-hot encoding and why it is not idealEmbedding vectors and dense representationsDefining a model with categorical featuresUsing the scale of the time seriesBalancing the sampling procedureVisualizing the data distributionTweaking the sampling procedureUsing and visualizing the dataloader with WeightedRandomSamplerSummaryFurther reading
Chapter 16: Specialized Deep Learning Architectures for Forecasting
Technical requirementsThe need for specialized architecturesNeural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)The architecture of N-BEATSForecasting with N-BEATSInterpreting N-BEATS forecastingNeural Basis Expansion Analysis for Interpretable Time Series Forecasting with Exogenous Variables (N-BEATSx)Handling exogenous variablesExogenous blocksNeural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)The Architecture of N-HiTSForecasting with N-HiTSInformerThe architecture of the Informer modelForecasting with the Informer modelAutoformerThe architecture of the Autoformer modelForecasting with AutoformerTemporal Fusion Transformer (TFT)The Architecture of TFTForecasting with TFTInterpreting TFTInterpretabilityProbabilistic forecastingProbability Density Function (PDF)Quantile functionsOther approachesSummaryReferencesFurther reading
Part 4 – Mechanics of Forecasting
Chapter 17: Multi-Step Forecasting
Why multi-step forecasting?Recursive strategyTraining regimeForecasting regimeDirect strategyTraining regimeForecasting regimeJoint strategyTraining regimeForecasting regimeHybrid strategiesDirRec StrategyIterative block-wise direct strategyRectify strategyRecJointHow to choose a multi-step forecasting strategy?SummaryReferences
Chapter 18: Evaluating Forecasts – Forecast Metrics
Technical requirementsTaxonomy of forecast error measuresIntrinsic metricsExtrinsic metricsInvestigating the error measuresLoss curves and complementarityBias towards over- or under-forecastingExperimental study of the error measuresUsing Spearman’s rank correlationGuidelines for choosing a metricSummaryReferencesFurther reading
Chapter 19: Evaluating Forecasts – Validation Strategies
Technical requirementsModel validationHoldout strategiesWindow strategyCalibration strategySampling strategyCross-validation strategiesChoosing a validation strategyValidation strategies for datasets with multiple time seriesSummaryReferencesFurther reading
Index
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Content preview from Modern Time Series Forecasting with Python

9 Ensembling and Stacking

In the previous chapter, we looked at a few machine learning algorithms and used them to generate forecasts on the London Smart Meters dataset. Now that we have multiple forecasts for all the households in the dataset, how do we come up with a single forecast by choosing or combining these different forecasts? That is what we will be doing in this chapter – we will learn how to leverage combinatorial and mathematical optimization to come up with a single forecast.

In this chapter, we will cover the following topics:

Strategies for combining forecasts
Stacking or blending

Technical requirements

You will need to set up the Anaconda environment following the instructions in the Preface of the book to get a working environment ...

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Modern Time Series Forecasting with Python

by Manu Joseph

9

Ensembling and Stacking

Technical requirements

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,
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