Multivariate Time-Series Prediction Using LSTM Neural Networks

机译：利用LSTM神经网络多变量时间序列预测

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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.

机译：在本文中，我们在多步时序列数据集中分析了LSTM神经网络的不同模型。本研究的目的是使用LSTM神经网络用于序列数据集来表达清晰精确的方法。这些模型可用于其他类似的数据集，并且模型被组成用于各种多步数据集，需要丝毫调整。本研究的主要目的和问题是是否有可能提供模型，以预测未来七天房屋消耗的电量。使用指定的模型，我们已经基于数据集进行了预测。我们还与不同模型中的方法获得的所有结果进行了全面的比较。在这项研究中，数据集是家庭电力消耗数据超过四年。我们已经能够在现有的最先进模型之间的误差中获得所需的预测结果。

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《International Computer Conference, Computer Society of Iran》|2021年|1-5|共5页
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Reza Ghanbari; Keivan Borna;
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Training; Analytical models; Recurrent neural networks; Power demand; Computational modeling; Time series analysis; Memory management;

机译：培训;分析模型;经常性神经网络;电力需求;计算建模;时间序列分析;内存管理;

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Multivariate Time-Series Prediction Using LSTM Neural Networks

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