PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning

no code implementations • 7 Nov 2023 • Hao liu, Jinrui Gan, Xiaoxuan Fan, Yi Zhang, Chuanxian Luo, Jing Zhang, Guangxin Jiang, Yucheng Qian, Changwei Zhao, Huan Ma, Zhenyu Guo

In this paper, we first point out that the unification of task objectives and adaptation for task difficulty are critical for bridging the gap between time series masked reconstruction and forecasting.

Representation Learning Self-Supervised Learning +1

An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development

no code implementations • 25 Mar 2022 • Jin Yang, Yingying Huang, Guangxin Jiang, Ying Chen

In the first component, we introduce a theoretical function-preserving transformation of recurrent neural networks (RNN) to the literature for capturing the hidden temporal patterns within the time-series data.

Neural Architecture Search Reinforcement Learning (RL) +2