LLMsForTimeSeries
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LLMsForTimeSeries is a repository that questions the usefulness of language models in time series forecasting. The work shows that simple baselines outperform most language model-based time series forecasting models. It includes ablation studies on LLM-based TSF methods and introduces the PAttn method, showcasing the performance of patching and attention structures in forecasting. The repository provides datasets, setup instructions, and scripts for running ablations on different datasets.
README:
In this work we showed that despite the recent popularity of LLMs in time series forecasting (TSF) they do not appear to meaningfully improve performance. A simple baseline, "PAttn," was proposed, which outperformed most LLM-based TSF models.
For reasoning (forecasting) time series through text. You may refer to this time series reasoning work.
Authors: Mingtian Tan,Mike A. Merrill,Vinayak Gupta,Tim Althoff,Thomas Hartvigsen
Autoregressive LLMs hold great potential for leveraging context to reason (forecast) future time series. Time Series Language Models mentioned in this paper are Non-Autoregressive.🌟 Papar Link
Recent work in time series analysis has increasingly focused on adapting pretrained large language models (LLMs) for forecasting (TSF), classification, and anomaly detection. These studies suggest that language models, designed for sequential dependencies in text, could generalize to time series data. While this idea aligns with the popularity of language models in machine learning, direct connections between language modeling and TSF remain unclear. How beneficial are language models for traditional TSF task?
Through a series of ablation studies on three recent LLM-based TSF methods, we found that removing the LLM component or replacing it with a simple attention layer did not worsen results—in many cases, it even led to improvements. Additionally, we introduced PAttn, showing that patching and attention structures can perform as well as state-of-the-art LLM-based forecasters.
You can access the well pre-processed datasets from Google Drive, then place the downloaded contents under ./datasets
Three different popular LLM-based TSF methods were included in our ablation approach. You might want to follow the corresponding repos, OneFitsAll, Time-LLM, and CALF, to set up the environment respectivly. For the ''PAttn'' method, the environment from any of the above repos is compatible.
The main difference between PAttn and PatchTST is that we gradually removed parts of the Transformer module that may not be as essential, and Position Embedding. For more explanation, please refer to this response.
Motivation: When DLinear has been surpassed by many new methods, we aim to provide a method based on Patching that is both simple and effective, serving as a simple baseline.
cd ./PAttn
bash ./scripts/ETTh.sh (for ETTh1 & ETTh2)
bash ./scripts/ETTm.sh (for ETTm1 & ETTm2)
bash ./scripts/weather.sh (for Weather)
cd ./CALF
sh scripts/long_term_forecasting/ETTh_GPT2.sh
sh scripts/long_term_forecasting/ETTm_GPT2.sh
sh scripts/long_term_forecasting/traffic.sh
(For other datasets, such as traffic)
cd ./OFA
bash ./script/ETTh_GPT2.sh
bash ./script/ETTm_GPT2.sh
bash ./script/illness.sh
(For other datasets, such as illness)
cd ./Time-LLM-exp
bash ./scripts/train_script/TimeLLM_ETTh1.sh
bash ./scripts/train_script/TimeLLM_ETTm1.sh
bash ./scripts/train_script/TimeLLM_Weather.sh
(For other datasets, such as Weather)
This codebase is built based on the Time-Series-Library. Thanks!
If you find our work useful, please kindly cite our work as follows:
@inproceedings{tan2024language,
title={Are Language Models Actually Useful for Time Series Forecasting?},
author={Tan, Mingtian and Merrill, Mike A and Gupta, Vinayak and Althoff, Tim and Hartvigsen, Thomas},
booktitle={Neural Information Processing Systems (NeurIPS)},
year={2024}
}
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