Inter-Series Transformer: Attending to Products in Time Series Forecasting

• URL: http://arxiv.org/abs/2408.03872v1
• Date: Wed, 7 Aug 2024 16:22:21 GMT
• Title: Inter-Series Transformer: Attending to Products in Time Series Forecasting
• Authors: Rares Cristian, Pavithra Harsha, Clemente Ocejo, Georgia Perakis, Brian Quanz, Ioannis Spantidakis, Hamza Zerhouni,
• Abstract summary: We develop a new Transformer-based forecasting approach using a shared, multi-task per-time series network. We provide a case study applying our approach to successfully improve demand prediction for a medical device manufacturing company.
• Score: 5.459207333107234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Generative Pretrained Hierarchical Transformer for Time Series Forecasting [3.739587363053192]
  We propose a novel generative pretrained hierarchical transformer architecture for forecasting, named textbfGPHT. We conduct sufficient experiments on eight datasets with mainstream self-supervised pretraining models and supervised models. The results demonstrated that GPHT surpasses the baseline models across various fine-tuning and zero/few-shot learning settings in the traditional long-term forecasting task.
  arXiv  Detail & Related papers  (2024-02-26T11:54:54Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting [13.790498420659636]
  Time series forecasting has received wide interest from existing research due to its broad applications inherent challenging. This paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable.
  arXiv  Detail & Related papers  (2023-07-13T13:16:01Z)
• Approaching sales forecasting using recurrent neural networks and transformers [57.43518732385863]
  We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.
  arXiv  Detail & Related papers  (2022-04-16T12:03:52Z)
• Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization [0.0]
  It is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. We propose a framework for forecasting short high-dimensional time series data by combining low-rank temporal matrix factorization and optimal model selection on latent time series.
  arXiv  Detail & Related papers  (2021-12-15T11:39:21Z)
• A machine learning approach for forecasting hierarchical time series [4.157415305926584]
  We propose a machine learning approach for forecasting hierarchical time series. Forecast reconciliation is the process of adjusting forecasts to make them coherent across the hierarchy. We exploit the ability of a deep neural network to extract information capturing the structure of the hierarchy.
  arXiv  Detail & Related papers  (2020-05-31T22:26:16Z)
• Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case [2.997238772148965]
  Time series data are prevalent in many scientific and engineering disciplines. We present a new approach to time series forecasting using Transformer-based machine learning models. We show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.
  arXiv  Detail & Related papers  (2020-01-23T00:22:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.