Multivariate Time Series Forecasting with Latent Graph Inference

03/07/2022
by   Victor Garcia Satorras, et al.
0

This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrated with current univariate methods. Our approach allows to trade-off accuracy and computational efficiency gradually via offering on one extreme inference of a potentially fully-connected graph or on another extreme a bipartite graph. In the potentially fully-connected case we consider all pair-wise interactions among time-series which yields the best forecasting accuracy. Conversely, the bipartite case leverages the dependency structure by inter-communicating the N time series through a small set of K auxiliary nodes that we introduce. This reduces the time and memory complexity w.r.t. previous graph inference methods from O(N^2) to O(NK) with a small trade-off in accuracy. We demonstrate the effectiveness of our model in a variety of datasets where both of its variants perform better or very competitively to previous graph inference methods in terms of forecasting accuracy and time efficiency.

READ FULL TEXT

page 8

page 18

research
09/10/2021

A Study of Joint Graph Inference and Forecasting

We study a recent class of models which uses graph neural networks (GNNs...
research
10/14/2020

Graph Deep Factors for Forecasting

Deep probabilistic forecasting techniques have recently been proposed fo...
research
02/02/2023

FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting

By investigating iterative methods for a constrained linear model, we pr...
research
07/30/2020

FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting

Forecasting of multivariate time-series is an important problem that has...
research
02/14/2019

Generalisation in fully-connected neural networks for time series forecasting

In this paper we study the generalisation capabilities of fully-connecte...
research
04/11/2023

The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Multivariate time series data comprises various channels of variables. T...
research
03/14/2023

Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data

The Internet of Things (IoT) system generates massive high-speed tempora...

Please sign up or login with your details

Forgot password? Click here to reset