mlsanityData to 7 Oct 2026

Unified Long-Term Time-Series Forecasting Benchmark

arXiv 2309.15946 ↗First posted 27 Sep 2023cs.LG cs.AI cs.NEcited by 1

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Abstract (arXiv)

In order to support the advancement of machine learning methods for predicting time-series data, we present a comprehensive dataset designed explicitly for long-term time-series forecasting. We incorporate a collection of datasets obtained from diverse, dynamic systems and real-life records. Each dataset is standardized by dividing it into training and test trajectories with predetermined lookback lengths. We include trajectories of length up to 2000 to ensure a reliable evaluation of long-term forecasting capabilities. To determine the most effective model in diverse scenarios, we conduct an extensive benchmarking analysis using classical and state-of-the-art models, namely LSTM, DeepAR, NLinear, N-Hits, PatchTST, and LatentODE. Our findings reveal intriguing performance comparisons among these models, highlighting the dataset-dependent nature of model effectiveness. Notably, we introduce a custom latent NLinear model and enhance DeepAR with a curriculum learning phase. Both consistently outperform their vanilla counterparts.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 27 Sep 2023.

Builds on

23 of 43 references.

References · 23

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11 more
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  1. 2025Foundation Models for Time Series: A Surveycited by 19