Details
| Original language | English |
|---|---|
| Title of host publication | NeurIPS Workshop on Time Series in the Age of Large Models |
| Publication status | E-pub ahead of print - 10 Jun 2024 |
Publication series
| Name | ArXiv |
|---|
Abstract
Keywords
- AutoML, Time Series Foreacsting, Neural Architecture Search
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NeurIPS Workshop on Time Series in the Age of Large Models. 2024. (ArXiv).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
AU - Deng, Difan
AU - Lindauer, Marius
PY - 2024/6/10
Y1 - 2024/6/10
N2 - The rapid development of time series forecasting research has brought many deep learning-based modules in this field. However, despite the increasing amount of new forecasting architectures, it is still unclear if we have leveraged the full potential of these existing modules within a properly designed architecture. In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks. With the design of a hierarchical search space, we incorporate many architecture types designed for forecasting tasks and allow for the efficient combination of different forecasting architecture modules. Results on long-term-time-series-forecasting tasks show that our approach can search for lightweight high-performing forecasting architectures across different forecasting tasks.
AB - The rapid development of time series forecasting research has brought many deep learning-based modules in this field. However, despite the increasing amount of new forecasting architectures, it is still unclear if we have leveraged the full potential of these existing modules within a properly designed architecture. In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks. With the design of a hierarchical search space, we incorporate many architecture types designed for forecasting tasks and allow for the efficient combination of different forecasting architecture modules. Results on long-term-time-series-forecasting tasks show that our approach can search for lightweight high-performing forecasting architectures across different forecasting tasks.
KW - AutoML
KW - Time Series Foreacsting
KW - Neural Architecture Search
M3 - Conference contribution
T3 - ArXiv
BT - NeurIPS Workshop on Time Series in the Age of Large Models
ER -