You're reading from Modern Time Series Forecasting with Python

Product typeBook

Published inNov 2022

PublisherPackt

ISBN-139781803246802

Edition1st Edition

Concepts

Data Science

Author (1)

Manu Joseph

References

Following are the references that we used in this chapter:

David Salinas, Valentin Flunkert, Jan Gasthaus, Tim Januschowski (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting. 36-3. 1181-1191: https://doi.org/10.1016/j.ijforecast.2019.07.001
Slawek Smyl (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting. 36-1: 75-85 https://doi.org/10.1016/j.ijforecast.2019.03.017
Montero-Manso, P., Hyndman, R.J.. (2020), Principles and algorithms for forecasting groups of time series: Locality and globality. arXiv:2008.00444[cs.LG]: https://arxiv.org/abs/2008.00444

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Modern Time Series Forecasting with Python

Published in: Nov 2022Publisher: PacktISBN-13: 9781803246802

Author (1)

Manu Joseph

Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son
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