فهرست مطالب

• Preface
• Part 1: Getting Familiar with Time Series
• Chapter 1: Introducing Time Series
• Technical requirements
• What is a time series?
• Data-generating process (DGP)
• 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
• Handling missing data
• Mapping additional information
• Saving and loading files to disk
• Handling longer periods of missing data
• Summary
• Chapter 3: Analyzing and Visualizing Time Series Data
• Technical requirements
• Components of a time series
• Visualizing time series data
• Decomposing a time series
• Detecting and treating outliers
• Summary
• References
• Further reading
• Chapter 4: Setting a Strong Baseline Forecast
• Technical requirements
• Setting up a test harness
• Generating strong baseline forecasts
• Assessing the forecastability of a time series
• Summary
• References
• Further reading
• Part 2: Machine Learning for Time Series
• Chapter 5: Time Series Forecasting as Regression
• Understanding the basics of machine learning
• Time series forecasting as regression
• Global forecasting models—a paradigm shift
• Summary
• References
• Further reading
• Chapter 6: Feature Engineering for Time Series Forecasting
• Technical requirements
• Understanding feature engineering
• Avoiding data leakage
• Setting a forecast horizon
• Time delay embedding
• Temporal embedding
• Summary
• Chapter 7: Target Transformations for Time Series Forecasting
• Technical requirements
• Detecting non-stationarity in time series
• Detecting and correcting for unit roots
• Detecting and correcting for trends
• Detecting and correcting for seasonality
• Detecting and correcting for heteroscedasticity
• 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
• Training and predicting for multiple households
• Summary
• References
• Further reading
• Chapter 9: Ensembling and Stacking
• Technical requirements
• Combining forecasts
• Stacking and blending
• Summary
• References
• Further reading
• Chapter 10: Global Forecasting Models
• Technical requirements
• Why Global Forecasting Models?
• Creating GFMs
• Strategies to improve GFMs
• 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?
• Components of a deep learning system
• 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
• Long short-term memory (LSTM) networks
• Gated recurrent unit (GRU)
• Convolution networks
• 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
• Summary
• Reference
• Further reading
• Chapter 14: Attention and Transformers for Time Series
• Technical requirements
• What is attention?
• The generalized attention model
• Forecasting with sequence-to-sequence models and attention
• Transformers—Attention is all you need
• Forecasting with Transformers
• Summary
• References
• Further reading
• Chapter 15: Strategies for Global Deep Learning Forecasting Models
• Technical requirements
• Creating global deep learning forecasting models
• Using time-varying information
• Using static/meta information
• Using the scale of the time series
• Balancing the sampling procedure
• Summary
• Further reading
• Chapter 16: Specialized Deep Learning Architectures for Forecasting
• Technical requirements
• The need for specialized architectures
• Introduction to NeuralForecast
• N-BEATS
• N-BEATSx
• N-HiTS
• Autoformer
• LTSF-Linear
• PatchTST
• iTransformer
• TFT
• TSMixer
• TiDE
• Summary
• References
• Further reading
• Chapter 17: Probabilistic Forecasting and More
• Probabilistic forecasting
• Road less traveled in time series forecasting
• Summary
• References
• Further reading
• Part 4: Mechanics of Forecasting
• Chapter 18: Multi-Step Forecasting
• Why multi-step forecasting?
• Standard notation
• Recursive strategy
• Direct strategy
• The Joint strategy
• Hybrid strategies
• How to choose a multi-step forecasting strategy
• Summary
• References
• Chapter 19: Evaluating Forecast Errors—A Survey of Forecast Metrics
• Technical requirements
• Taxonomy of forecast error measures
• Investigating the error measures
• Experimental study of the error measures
• Guidelines for choosing a metric
• Summary
• References
• Further reading
• Chapter 20: Evaluating Forecasts—Validation Strategies
• Technical requirements
• Model validation
• Holdout strategies
• Cross-validation strategies
• Choosing a validation strategy
• Validation strategies for datasets with multiple time series
• Summary
• References
• Further reading
• Other Books You May Enjoy
• Index