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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.02525 |
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| _version_ | 1866916505995706368 |
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| author | Zhang, Hanyu Arvin, Chuck Efimov, Dmitry Mahoney, Michael W. Perrault-Joncas, Dominique Ramasubramanian, Shankar Wilson, Andrew Gordon Wolff, Malcolm |
| author_facet | Zhang, Hanyu Arvin, Chuck Efimov, Dmitry Mahoney, Michael W. Perrault-Joncas, Dominique Ramasubramanian, Shankar Wilson, Andrew Gordon Wolff, Malcolm |
| contents | Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to obvious model failures; for example, models may be unaware of the details of a particular product, and hence fail to anticipate seasonal surges in customer demand in the lead up to major exogenous events like holidays for clearly relevant products. To address this shortcoming, this paper introduces a novel forecast post-processor -- which we call LLMForecaster -- that fine-tunes large language models (LLMs) to incorporate unstructured semantic and contextual information and historical data to improve the forecasts from an existing demand forecasting pipeline. In an industry-scale retail application, we demonstrate that our technique yields statistically significantly forecast improvements across several sets of products subject to holiday-driven demand surges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Zhang, Hanyu Arvin, Chuck Efimov, Dmitry Mahoney, Michael W. Perrault-Joncas, Dominique Ramasubramanian, Shankar Wilson, Andrew Gordon Wolff, Malcolm Machine Learning Computation and Language Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to obvious model failures; for example, models may be unaware of the details of a particular product, and hence fail to anticipate seasonal surges in customer demand in the lead up to major exogenous events like holidays for clearly relevant products. To address this shortcoming, this paper introduces a novel forecast post-processor -- which we call LLMForecaster -- that fine-tunes large language models (LLMs) to incorporate unstructured semantic and contextual information and historical data to improve the forecasts from an existing demand forecasting pipeline. In an industry-scale retail application, we demonstrate that our technique yields statistically significantly forecast improvements across several sets of products subject to holiday-driven demand surges. |
| title | LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2412.02525 |