Related papers: Long Range Probabilistic Forecasting in Time-Series using High Order Statistics

Long Range Probabilistic Forecasting in Time-Series using High Order Statistics

URL: http://arxiv.org/abs/2111.03394v1
Date: Fri, 5 Nov 2021 11:10:35 GMT
Title: Long Range Probabilistic Forecasting in Time-Series using High Order Statistics
Authors: Prathamesh Deshpande, Sunita Sarawagi
Abstract summary: We present a novel probabilistic forecasting method that produces forecasts coherent in terms of base level and predicted aggregate statistics. We show that our method improves forecast performance across both base level and unseen aggregates post inference on real datasets ranging three diverse domains.
Score: 19.12411040726229
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Long range forecasts are the starting point of many decision support systems that need to draw inference from high-level aggregate patterns on forecasted values. State of the art time-series forecasting methods are either subject to concept drift on long-horizon forecasts, or fail to accurately predict coherent and accurate high-level aggregates. In this work, we present a novel probabilistic forecasting method that produces forecasts that are coherent in terms of base level and predicted aggregate statistics. We achieve the coherency between predicted base-level and aggregate statistics using a novel inference method. Our inference method is based on KL-divergence and can be solved efficiently in closed form. We show that our method improves forecast performance across both base level and unseen aggregates post inference on real datasets ranging three diverse domains.

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