Modern Time Series Forecasting with Python
Data Science
Book Details
Book Title
Modern Time Series Forecasting with Python
Author
Manu Joseph, Jeffrey Tackes
Publisher
Papercut Limited
Publication Date
2024
ISBN
9781835883181
Number of Pages
659
Language
English
Format
File Size
13.7MB
Subject
Data science/Algorithms and Data Structures: Pattern Recognition
Table of Contents
- 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