Aidenzich
commented
3 months ago | Why (Problem/Need) | What (Solution/Tool) | How (Method/Approach) |
|---|---|---|
| Time-series forecasting is crucial across various domains like retail, finance, and healthcare. Traditional forecasting models and even recent deep-learning approaches face limitations in handling diverse datasets with efficiency and accuracy. | The paper introduces TimesFM, a decoder-only foundation model designed for time-series forecasting. This model aims to achieve high accuracy in zero-shot forecasting across diverse datasets without the need for dataset-specific training. | TimesFM is pre-trained on a large corpus of real-world and synthetic time-series data. It utilizes a decoder-style attention model with input patching to learn temporal patterns effectively. This approach allows TimesFM to handle varying context lengths, prediction lengths, and time granularities during inference. |
https://arxiv.org/pdf/2310.10688.pdf