A Multistep Multivariate Fuzzy-Based Time-Series Forecasting on Internet of Things Data

Multistep ahead time series forecasting is essential in Internet of Things (IoT) applications in smart cities and smart homes to make accurate future predictions and precise decision making. Thus, this study introduces a novel multiple-input single-output (MISO) forecasting method called Multistep Embedding-based fuzzy time series (MS-EFTS), designed to predict high-dimensional nonstationary time series data. As a first-order approach, it employs a direct strategy that integrates an embedding transformation with a weighted multivariate FTS (WMVFTS) model. This combination allows for effective predictions over long-term horizons within low-dimensional, learned continuous representations. The effectiveness of the proposed MS-EFTS is assessed using three high-dimensional IoT time series in this investigation. The obtained results showcase the superior performance of the proposed method compared to some deep learning forecasting methods, including LSTM, BiLSTM, TCN, and CNN-LSTM, in terms of accuracy, parsimony, and efficiency.