MECATS: Mixture-of-Experts for Quantile Forecasts of Aggregated Time Series

• URL: http://arxiv.org/abs/2112.11669v1
• Date: Wed, 22 Dec 2021 05:05:30 GMT
• Title: MECATS: Mixture-of-Experts for Quantile Forecasts of Aggregated Time Series
• Authors: Xing Han, Jing Hu, Joydeep Ghosh
• Abstract summary: We introduce a mixture of heterogeneous experts framework called textttMECATS. It simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy. Different types of forecasting models can be employed as individual experts so that the form of each model can be tailored to the nature of the corresponding time series.
• Score: 11.826510794042548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a mixture of heterogeneous experts framework called \texttt{MECATS}, which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy. Different types of forecasting models can be employed as individual experts so that the form of each model can be tailored to the nature of the corresponding time series. \texttt{MECATS} learns hierarchical relationships during the training stage to help generalize better across all the time series being modeled and also mitigates coherency issues that arise due to constraints imposed by the hierarchy. We further build multiple quantile estimators on top of the point forecasts. The resulting probabilistic forecasts are nearly coherent, distribution-free, and independent of the choice of forecasting models. We conduct a comprehensive evaluation on both point and probabilistic forecasts and also formulate an extension for situations where change points exist in sequential data. In general, our method is robust, adaptive to datasets with different properties, and highly configurable and efficient for large-scale forecasting pipelines.

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)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting [4.716034416800441]
  This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR) EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), is suitable for nonstationary and heteroscedastic time series data, and can be applied on top of any forecasting model. The results demonstrate that EnCQR outperforms models based only on quantile regression or conformal prediction, and it provides sharper, more informative, and valid PIs.
  arXiv  Detail & Related papers  (2022-02-17T16:54:20Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
  We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T20:41:19Z)
• Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures [2.1670528702668648]
  We present a novel method capable of accurate and coherent probabilistic forecasts for time series when reliable hierarchical information is present. We call it Deep Poisson Mixture Network (DPMN) It relies on the combination of neural networks and a statistical model for the joint distribution of the hierarchical time series structure.
  arXiv  Detail & Related papers  (2021-10-25T18:02:03Z)
• Hierarchically Regularized Deep Forecasting [18.539846932184012]
  We propose a new approach for hierarchical forecasting based on decomposing the time series along a global set of basis time series. Unlike past methods, our approach is scalable at inference-time while preserving coherence among the time series forecasts.
  arXiv  Detail & Related papers  (2021-06-14T17:38:16Z)
• Predicting Temporal Sets with Deep Neural Networks [50.53727580527024]
  We propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective is to learn element relationship by constructing set-level co-occurrence graph. We design an attention-based module to adaptively learn the temporal dependency of elements and sets.
  arXiv  Detail & Related papers  (2020-06-20T03:29:02Z)

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.