Deep Double Descent for Time Series Forecasting: Avoiding Undertrained
Models

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Deep Double Descent for Time Series Forecasting: Avoiding Undertrained Models

2 November 2023

Valentino Assandri

Burhaneddin Yaman

Ariel Emiliano Repetur

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Papers citing "Deep Double Descent for Time Series Forecasting: Avoiding Undertrained Models"

10 / 10 papers shown

Title
Ti-MAE: Self-Supervised Masked Time Series Autoencoders Zhe Li Zhongwen Rao Lujia Pan Pengyun Wang Zenglin Xu AI4TS 65 53 0 21 Jan 2023
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency Xiang Zhang Ziyuan Zhao Theodoros Tsiligkaridis Marinka Zitnik AI4TS 129 289 0 17 Jun 2022
Training Compute-Optimal Large Language Models Jordan Hoffmann Sebastian Borgeaud A. Mensch Elena Buchatskaya Trevor Cai ... Karen Simonyan Erich Elsen Jack W. Rae Oriol Vinyals Laurent Sifre AI4TS 208 1,980 0 29 Mar 2022
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting Tian Zhou Ziqing Ma Qingsong Wen Xue Wang Liang Sun Rong Jin AI4TS 248 1,435 0 30 Jan 2022
A Universal Law of Robustness via Isoperimetry Sébastien Bubeck Mark Sellke 50 218 0 26 May 2021
Do We Really Need Deep Learning Models for Time Series Forecasting? Shereen Elsayed Daniela Thyssens Ahmed Rashed H. Jomaa Lars Schmidt-Thieme AI4TS 56 107 0 06 Jan 2021
Time Series Data Augmentation for Deep Learning: A Survey Qingsong Wen Liang Sun Fan Yang Xiaomin Song Jing Gao Xue Wang Huan Xu AI4TS 73 644 0 27 Feb 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 611 4,905 0 23 Jan 2020
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case Neo Wu Bradley Green X. Ben S. O’Banion AI4TS 73 455 0 23 Jan 2020
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting Bryan Lim Sercan O. Arik Nicolas Loeff Tomas Pfister AI4TS 125 1,470 0 19 Dec 2019