Transformers for Time Series Forecasting. Modern techniques for time series forecasting, classification, and anomaly detection with transformers - Helion
Tytuł oryginału: Transformers for Time Series Forecasting. Modern techniques for time series forecasting, classification, and anomaly detection with transformers
ISBN: 9781805122036
Format: ebook
Księgarnia: Helion
Cena książki: 4,90 zł
Książka będzie dostępna od października 2023
Generative AI has profoundly changed the world, and Transformers are a crucial instrument in this process. However, the application of Transformers for time series hasn't been widely adopted yet, despite the immense potential in this field. Transformers, among other things, possess the ability to identify long-range dependencies and interactions in the data.
In the Transformers for Time Series Forecasting book, the most recent research findings are presented in a highly practical fashion. Utilizing real-life projects and employing PyTorch and TensorFlow, the reader is guided through various use cases. Starting with the most commonly utilised applications for time series data, such as forecasting and classification, the book introduces the reader to both the theory and implementation. Later, more specialised cases are covered, including anomaly detection, event forecasting, and spatio-temporal modelling.
The final chapters introduce how to improve these algorithms further, what the best practices are, how to optimise with hyperparameter tuning techniques and architecture-level modifications. Lastly, we discuss how to scale transformer-based solutions when dealing with large amounts of data.
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Spis treści
Transformers for Time Series Forecasting. Modern techniques for time series forecasting, classification, and anomaly detection with transformers eBook -- spis treści
- 1. Time Series Analysis: Challenges
- 2. Transformers for Time Series
- 3. Time Series Forecasting with Transformers
- 4. Transformers for Time Series Classification
- 5. Spatio-Temporal Modelling leveraging Transformers
- 6. Event Forecasting using Transformers
- 7. Applying Transformers for Time Series Anomaly Detection
- 8. Tips, Best Practices for Efficient Transformer Implementation
- 9. Hyperparameter tuning, Architecture-level Modifications, AutoML, NAS (Neural Architecture Search)
- 10. Scaling Transformers