Ghanbari Amoughin R (2021)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2021
Pages Range: 1-5
Conference Proceedings Title: 2021 26th International Computer Conference, Computer Society of Iran (CSICC)
DOI: 10.1109/CSICC52343.2021.9420543
In this paper, we analyzed different models of LSTM neural networks on the multi-step time-series dataset. The purpose of this study is to express a clear and precise method using LSTM neural networks for sequence datasets. These models can be used in other similar datasets, and the models are composed to be developed for various multi-step datasets with the slightest adjustment required. The principal purpose and question of this study were whether it is possible to provide a model to predict the amount of electricity consumed by a house over the next seven days. Using the specified models, we have made a prediction based on the dataset. We also made a comprehensive comparison with all the results obtained from the methods among different models. In this study, the dataset is household electricity consumption data gathered over four years. We have been able to achieve the desired prediction results with the least amount of error among the existing state-of-the-art models.
APA:
Ghanbari Amoughin, R. (2021). Multivariate Time-Series Prediction Using LSTM Neural Networks. In 2021 26th International Computer Conference, Computer Society of Iran (CSICC) (pp. 1-5).
MLA:
Ghanbari Amoughin, Reza. "Multivariate Time-Series Prediction Using LSTM Neural Networks." Proceedings of the 2021 26th International Computer Conference, Computer Society of Iran (CSICC) 2021. 1-5.
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