Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning

Cover
Title Page
Copyright and Credits
Dedication
Contributors
Table of Contents
Preface
Part 1 – Getting Familiar with Time Series
Chapter 1: Introducing Time Series
	Technical requirements
	What is a time series?
		Types of time series
		Main areas of application for time series analysis
	Data-generating process (DGP)
		Generating synthetic time series
		Stationary and non-stationary time series
	What can we forecast?
	Forecasting terminology
	Summary
	Further reading
Chapter 2: Acquiring and Processing Time Series Data
	Technical requirements
	Understanding the time series dataset
		Preparing a data model
	pandas datetime operations, indexing, and slicing – a refresher
		Converting the date columns into pd.Timestamp/DatetimeIndex
		Using the .dt accessor and datetime properties
		Slicing and indexing
		Creating date sequences and managing date offsets
	Handling missing data
		Converting the half-hourly block-level data (hhblock) into time series data
		Compact, expanded, and wide forms of data
		Enforcing regular intervals in time series
		Converting the London Smart Meters dataset into a time series format
	Mapping additional information
	Saving and loading files to disk
	Handling longer periods of missing data
		Imputing with the previous day
		Hourly average profile
		The hourly average for each weekday
		Seasonal interpolation
	Summary
Chapter 3: Analyzing and Visualizing Time Series Data
	Technical requirements
	Components of a time series
		The trend component
		The seasonal component
		The cyclical component
		The irregular component
	Visualizing time series data
		Line charts
		Seasonal plots
		Seasonal box plots
		Calendar heatmaps
		Autocorrelation plot
	Decomposing a time series
		Detrending
		Deseasonalizing
		Implementations
	Detecting and treating outliers
		Standard deviation
		Interquartile range (IQR)
		Isolation Forest
		Extreme studentized deviate (ESD) and seasonal ESD (S-ESD)
		Treating outliers
	Summary
	References
	Further reading
Chapter 4: Setting a Strong Baseline Forecast
	Technical requirements
	Setting up a test harness
		Creating holdout (test) and validation datasets
		Choosing an evaluation metric
	Generating strong baseline forecasts
		Naïve forecast
		Moving average forecast
		Seasonal naive forecast
		Exponential smoothing (ETS)
		ARIMA
		Theta Forecast
		Fast Fourier Transform forecast
		Evaluating the baseline forecasts
	Assessing the forecastability of a time series
		Coefficient of Variation (CoV)
		Residual variability (RV)
		Entropy-based measures
		Kaboudan metric
	Summary
	References
	Further reading
Part 2 – Machine Learning for Time Series
Chapter 5: Time Series Forecasting as Regression
	Understanding the basics of machine learning
		Supervised machine learning tasks
		Overfitting and underfitting
		Hyperparameters and validation sets
	Time series forecasting as regression
		Time delay embedding
		Temporal embedding
	Global forecasting models – a paradigm shift
	Summary
	References
	Further reading
Chapter 6: Feature Engineering for Time Series Forecasting
	Technical requirements
	Feature engineering
	Avoiding data leakage
	Setting a forecast horizon
	Time delay embedding
		Lags or backshift
		Rolling window aggregations
		Seasonal rolling window aggregations
		Exponentially weighted moving averages (EWMA)
	Temporal embedding
		Calendar features
		Time elapsed
		Fourier terms
	Summary
Chapter 7: Target Transformations for Time Series Forecasting
	Technical requirements
	Handling non-stationarity in time series
	Detecting and correcting for unit roots
		Unit roots
		The Augmented Dickey-Fuller (ADF) test
		Differencing transform
	Detecting and correcting for trends
		Deterministic and stochastic trends
		Kendall’s Tau
		Mann-Kendall test (M-K test)
		Detrending transform
	Detecting and correcting for seasonality
		Detecting seasonality
		Deseasonalizing transform
	Detecting and correcting for heteroscedasticity
		Detecting heteroscedasticity
		Log transform
		Box-Cox transform
	AutoML approach to target transformation
	Summary
	References
	Further reading
Chapter 8: Forecasting Time Series with Machine Learning Models
	Technical requirements
	Training and predicting with machine learning models
	Generating single-step forecast baselines
	Standardized code to train and evaluate machine learning models
		FeatureConfig
		MissingValueConfig
		ModelConfig
		MLForecast
		Helper functions for evaluating models
		Linear regression
		Regularized linear regression
		Decision trees
		Random forest
		Gradient boosting decision trees
	Training and predicting for multiple households
		Using AutoStationaryTransformer
	Summary
	References
	Further reading
Chapter 9: Ensembling and Stacking
	Technical requirements
	Combining forecasts
		Best fit
		Measures of central tendency
		Simple hill climbing
		Stochastic hill climbing
		Simulated annealing
		Optimal weighted ensemble
	Stacking or blending
	Summary
	References
	Further reading
Chapter 10: Global Forecasting Models
	Technical requirements
	Why Global Forecasting Models (GFMs)?
		Sample size
		Cross-learning
		Multi-task learning
		Engineering complexity
	Creating GFMs
	Strategies to improve GFMs
		Increasing memory
		Using time series meta-features
		Tuning hyperparameters
		Partitioning
	Bonus – interpretability
	Summary
	References
	Further reading
Part 3 – Deep Learning for Time Series
Chapter 11: Introduction to Deep Learning
	Technical requirements
	What is deep learning and why now?
		Why now?
		What is deep learning?
		Perceptron – the first neural network
	Components of a deep learning system
		Representation learning
		Linear transformation
		Activation functions
		Output activation functions
		Loss function
		Forward and backward propagation
	Summary
	References
	Further reading
Chapter 12: Building Blocks of Deep Learning for Time Series
	Technical requirements
	Understanding the encoder-decoder paradigm
	Feed-forward networks
	Recurrent neural networks
		The RNN layer in PyTorch
	Long short-term memory (LSTM) networks
		The LSTM layer in PyTorch
	Gated recurrent unit (GRU)
		The GRU layer in PyTorch
	Convolution networks
		Convolution
		Padding, stride, and dilations
		The convolution layer in PyTorch
	Summary
	References
	Further reading
Chapter 13: Common Modeling Patterns for Time Series
	Technical requirements
	Tabular regression
	Single-step-ahead recurrent neural networks
	Sequence-to-sequence (Seq2Seq) models
		RNN-to-fully connected network
		RNN-to-RNN
	Summary
	Reference
	Further reading
Chapter 14: Attention and Transformers for Time Series
	Technical requirements
	What is attention?
	The generalized attention model
		Alignment functions
		The distribution function
	Forecasting with sequence-to-sequence models and attention
	Transformers – Attention is all you need
		Attention is all you need
		Transformers in time series
	Forecasting with Transformers
	Summary
	References
	Further reading
Chapter 15: Strategies for Global Deep Learning Forecasting Models
	Technical requirements
	Creating global deep learning forecasting models
		Preprocessing the data
		Understanding TimeSeriesDataset from PyTorch Forecasting
		Building the first global deep learning forecasting model
	Using time-varying information
	Using static/meta information
		One-hot encoding and why it is not ideal
		Embedding vectors and dense representations
		Defining a model with categorical features
	Using the scale of the time series
	Balancing the sampling procedure
		Visualizing the data distribution
		Tweaking the sampling procedure
		Using and visualizing the dataloader with WeightedRandomSampler
	Summary
	Further reading
Chapter 16: Specialized Deep Learning Architectures for Forecasting
	Technical requirements
	The need for specialized architectures
	Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)
		The architecture of N-BEATS
		Forecasting with N-BEATS
		Interpreting N-BEATS forecasting
	Neural Basis Expansion Analysis for Interpretable Time Series Forecasting with Exogenous Variables (N-BEATSx)
		Handling exogenous variables
		Exogenous blocks
	Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)
		The Architecture of N-HiTS
		Forecasting with N-HiTS
	Informer
		The architecture of the Informer model
		Forecasting with the Informer model
	Autoformer
		The architecture of the Autoformer model
		Forecasting with Autoformer
	Temporal Fusion Transformer (TFT)
		The Architecture of TFT
		Forecasting with TFT
		Interpreting TFT
	Interpretability
	Probabilistic forecasting
		Probability Density Function (PDF)
		Quantile functions
		Other approaches
	Summary
	References
	Further reading
Part 4 – Mechanics of Forecasting
Chapter 17: Multi-Step Forecasting
	Why multi-step forecasting?
	Recursive strategy
		Training regime
		Forecasting regime
	Direct strategy
		Training regime
		Forecasting regime
	Joint strategy
		Training regime
		Forecasting regime
	Hybrid strategies
		DirRec Strategy
		Iterative block-wise direct strategy
		Rectify strategy
		RecJoint
	How to choose a multi-step forecasting strategy?
	Summary
	References
Chapter 18: Evaluating Forecasts – Forecast Metrics
	Technical requirements
	Taxonomy of forecast error measures
		Intrinsic metrics
		Extrinsic metrics
	Investigating the error measures
		Loss curves and complementarity
		Bias towards over- or under-forecasting
	Experimental study of the error measures
		Using Spearman’s rank correlation
	Guidelines for choosing a metric
	Summary
	References
	Further reading
Chapter 19: Evaluating Forecasts – Validation Strategies
	Technical requirements
	Model validation
	Holdout strategies
		Window strategy
		Calibration strategy
		Sampling strategy
	Cross-validation strategies
	Choosing a validation strategy
	Validation strategies for datasets with multiple time series
	Summary
	References
	Further reading
Index
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