High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series

TL;DR

This paper introduces a novel embedding-based fuzzy time series method for high-dimensional, non-stationary IoT data, improving forecasting accuracy and handling concept drift effectively.

Contribution

It proposes a new approach combining data embedding with fuzzy time series to better model complex, high-dimensional non-stationary time series in IoT applications.

Findings

Explains 98% of variance in data

Achieves 11.52% RMSE in forecasts

Handles concept drift effectively

Abstract

In Internet of things (IoT), data is continuously recorded from different data sources and devices can suffer faults in their embedded electronics, thus leading to a high-dimensional data sets and concept drift events. Therefore, methods that are capable of high-dimensional non-stationary time series are of great value in IoT applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. Unfortunately, FTS encounters difficulties when dealing with data sets of many variables and scenarios with concept drift. We present a new approach to handle high-dimensional non-stationary time series, by projecting the original high-dimensional data into a low dimensional embedding space and using FTS approach. Combining these techniques enables a better representation of the complex content of non-stationary multivariate time series and accurate forecasts. Our model is able to explain 98% of the variance and reach 11.52% of RMSE, 2.68% of MAE and 2.91% of MAPE.