Papers by Harshavardhan Kamarthi
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting (2024.findings-acl)
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| Challenge: | Existing prompting methods oversimplify time-series forecasting (TSF) time-Series data are ubiquitous across various domains, including public health, finance and energy. |
| Approach: | They propose a method for prompting off-the-shelf Large Language Models (LLMs) they decompose TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each . |
| Outcome: | The proposed approach decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. |