Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications.
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2023
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Resumo
High-dimensional time series increasingly arise in the Internet of Energy (IoE), given the use of multi-sensor
environments and the two way communication between energy consumers and the smart grid. Therefore,
methods that are capable of computing high-dimensional time series are of great value in smart building and
IoE applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy
implementation and high accuracy. Unfortunately, the existing FTS models can be unfeasible if all variables
were used to train the model. We present a new methodology named Embedding Fuzzy Time Series (EFTS),
by applying a combination of data embedding transformation and FTS methods. The EFTS is an explainable
and data-driven approach, which is flexible and adaptable for many smart building and IoE applications.
The experimental results with three public datasets show that our methodology outperforms several machine
learning based forecasting methods (LSTM, GRU, TCN, RNN, MLP and GBM), and demonstrates the accuracy
and parsimony of the EFTS in comparison to the baseline methods and the results previously published in the
literature, showing an enhancement greater than 80%. Therefore, EFTS has a great value in high-dimensional
time series forecasting in IoE applications.
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Multivariate time series, Embedding transformation, Smart buildings
Citação
BITENCOURT, H. V. et al. Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications. Energy, v. 271, artigo 127072, maio 2023. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0360544223004668>. Acesso em: 06 jul. 2023.