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| Title | A Multi-Step Multivariate Fuzzy-Based Time Series Forecasting On Internet Of Things Data |
|---|---|
| ID_Doc | 2859 |
| Authors | Bitencourt H.V.; Lucas P.-C.D.O.; Orang O.; Silva P.C.L.; Guimaraes F.G. |
| Year | 2025 |
| Published | IEEE Internet of Things Journal |
| DOI | http://dx.doi.org/10.1109/JIOT.2025.3549715 |
| Abstract | Multi-step 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 Multi-Step Embedding-based Fuzzy Time Series (MS-EFTS), designed to predict high-dimensional non-stationary 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. © 2014 IEEE. |
| Author Keywords | embedding transformation; fuzzy time series; multi-step-ahead time series forecasting; Smart buildings; smart cities |
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