Previous Card
How OpenAI uses Codex
TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING
TEMPO is a novel framework that utilizes a prompt-based generative pre-trained transformer for time series forecasting. It effectively learns time series representations by decomposing data into trend, seasonal, and residual components and introducing prompt design for distribution adaptation. The model demonstrates superior zero-shot performance and handles multimodal inputs, highlighting its potential as a foundational model for diverse temporal phenomena. ✨
Article Points:
1
TEMPO is a prompt-based GPT for time series forecasting.
2
It decomposes time series into trend, seasonal, and residual components.
3
Prompt design facilitates distribution adaptation across diverse time series.
4
Achieves superior zero-shot performance on benchmark datasets.
5
Effectively leverages multimodal inputs, including textual information.
6
Paves the way for foundational models in time series forecasting.
TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING
Core Idea
Prompt-based GPT
Generative Pre-trained Transformer
Key Inductive Biases
Decomposition (Trend, Season, Residual)
Prompt Design (Distribution Adaptation)
Architecture
GPT-2 Backbone
STL Decomposition
Soft Prompting & LoRA
Performance
Superior Zero-Shot Forecasting
Multimodal Input Handling
State-of-the-art Results
Future Potential