High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series
This addresses forecasting challenges in IoT for applications with high-dimensional data and concept drift, representing an incremental improvement by combining existing techniques.
The paper tackled the problem of forecasting high-dimensional non-stationary multivariate time series in IoT applications, where data suffers from concept drift and many variables, by projecting data into a low-dimensional embedding space and using Fuzzy Time Series, achieving 98% explained variance and error metrics like 11.52% RMSE.
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.