A weighted multivariate fuzzy time series method for multiple output high-dimensional time series forecasting in IoT applications

Hugo Vinicius Bitencourt, Patrícia de Oliveira e Lucas, Omid Orang, Petrônio C. L. Silva, Frederico Gadelha Guimarães

Abstract: In Internet of Things (IoT) applications, data flows are continuous streams of high-dimensional time series that aggregate various data sources. In this context, decision-making processes frequently encompass multiple factors and criteria that demand forecasting these time series. This paper introduces MO-WMVFTS, a novel multiple-input multiple-output (MIMO) fuzzy time series (FTS) method for tackling this complex scenario. MO-WMVFTS is a hybrid forecasting method that fuses weighted multivariate FTS (WMVFTS) with embedding transformations, designed for IoT applications. To assess the performance of the proposed method, it was applied to the prediction of energy consumption in smart homes and air quality in smart cities. Here, three real-world datasets are used to assess the validity of our proposed approach, and the results obtained are highly competitive when compared to other existing methods.

External IDs:dblp:conf/lacci/BitencourtLOSG24