Time Series Forecasting via Learning Convolutionally Low-Rank Models

TL;DR

This paper introduces a learning-based convolutionally low-rank model for time series forecasting that adapts to complex series with trends and dynamics, outperforming existing methods through extensive real-world experiments.

Contribution

It proposes LbCNNM, a novel model integrating learnable transformations into CNNM, enabling effective forecasting of diverse time series components.

Findings

LbCNNM achieves superior forecasting accuracy on large real-world datasets.

The model effectively handles trends, seasonality, and dynamics in time series.

Extensive experiments validate the robustness and versatility of LbCNNM.

Abstract

Recently, Liu and Zhang studied the rather challenging problem of time series forecasting from the perspective of compressed sensing. They proposed a no-learning method, named Convolution Nuclear Norm Minimization (CNNM), and proved that CNNM can exactly recover the future part of a series from its observed part, provided that the series is convolutionally low-rank. While impressive, the convolutional low-rankness condition may not be satisfied whenever the series is far from being seasonal, and is in fact brittle to the presence of trends and dynamics. This paper tries to approach the issues by integrating a learnable, orthonormal transformation into CNNM, with the purpose for converting the series of involute structures into regular signals of convolutionally low-rank. We prove that the resultant model, termed Learning-Based CNNM (LbCNNM), strictly succeeds in identifying the future part of a series, as long as the transform of the series is convolutionally low-rank. To learn proper transformations that may meet the required success conditions, we devise an interpretable method based on Principal Component Pursuit (PCP). Equipped with this learning method and some elaborate data argumentation skills, LbCNNM not only can handle well the major components of time series (including trends, seasonality and dynamics), but also can make use of the forecasts provided by some other forecasting methods; this means LbCNNM can be used as a general tool for model combination. Extensive experiments on 100,452 real-world time series from Time Series Data Library (TSDL) and M4 Competition (M4) demonstrate the superior performance of LbCNNM.