Inter-Series Transformer: Attending to Products in Time Series Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913460653129728 |
|---|---|
| author | Cristian, Rares Harsha, Pavithra Ocejo, Clemente Perakis, Georgia Quanz, Brian Spantidakis, Ioannis Zerhouni, Hamza |
| author_facet | Cristian, Rares Harsha, Pavithra Ocejo, Clemente Perakis, Georgia Quanz, Brian Spantidakis, Ioannis Zerhouni, Hamza |
| contents | Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have shown promising results in forecasting on common time series benchmark datasets. However, application to supply chain demand forecasting, which can have challenging characteristics such as sparsity and cross-series effects, has been limited.
In this work, we explore the application of Transformer-based models to supply chain demand forecasting. In particular, we develop a new Transformer-based forecasting approach using a shared, multi-task per-time series network with an initial component applying attention across time series, to capture interactions and help address sparsity. We provide a case study applying our approach to successfully improve demand prediction for a medical device manufacturing company. To further validate our approach, we also apply it to public demand forecasting datasets as well and demonstrate competitive to superior performance compared to a variety of baseline and state-of-the-art forecast methods across the private and public datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inter-Series Transformer: Attending to Products in Time Series Forecasting Cristian, Rares Harsha, Pavithra Ocejo, Clemente Perakis, Georgia Quanz, Brian Spantidakis, Ioannis Zerhouni, Hamza Machine Learning Artificial Intelligence I.2.6; G.3; I.5.1 Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have shown promising results in forecasting on common time series benchmark datasets. However, application to supply chain demand forecasting, which can have challenging characteristics such as sparsity and cross-series effects, has been limited. In this work, we explore the application of Transformer-based models to supply chain demand forecasting. In particular, we develop a new Transformer-based forecasting approach using a shared, multi-task per-time series network with an initial component applying attention across time series, to capture interactions and help address sparsity. We provide a case study applying our approach to successfully improve demand prediction for a medical device manufacturing company. To further validate our approach, we also apply it to public demand forecasting datasets as well and demonstrate competitive to superior performance compared to a variety of baseline and state-of-the-art forecast methods across the private and public datasets. |
| title | Inter-Series Transformer: Attending to Products in Time Series Forecasting |
| topic | Machine Learning Artificial Intelligence I.2.6; G.3; I.5.1 |
| url | https://arxiv.org/abs/2408.03872 |