This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, ...

The first part of this article systematically reviews the Transformer model while highlighting its strengths and limitations.

Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers.

In recent years many new algorithms have been developed for applications in speech and image processing which may be repurposed for time series prediction.

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.

Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book.

... models: an application to time series forecasting. Neural Processing Letters, 13.2, 115–133. 13. Kumpati, S. N. ... Transformer Models for Time Series Forecasting: The Influenza Prevalence Case. arXiv:2001.08317. Concluding Remarks ...

This book constitutes the refereed proceedings of the 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020, which was cancelled due to the COVID-19 pandemic, amalgamated with CAEPIA 2021, and held in Malaga, ...

This book demystifies the technique, providing readers with little or no time series or machine learning experience the fundamental tools required to create and evaluate time series models.

The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.