TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

arXiv:2310.04948v3 Announce Type: replace
Abstract: The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrated impressive performance via training one general-purpose model across various textual datasets. It is intriguing to explore whether GPT-type architectures can be effective for time series, capturing the intrinsic dynamic attributes and leading to significant accuracy improvements. …

abstract advances applications architectures art arxiv cs.cl cs.lg deep learning domains forecasting general generative generative pre-trained transformer gpt language language processing modeling natural natural language natural language processing performance processing prompt results series state time series time series forecasting training transformer type via