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首页> 外文会议>International Conference on Artificial Intelligence and Security >Implementation of Multiplicative Seasonal ARIMA Modeling and Flood Prediction Based on Long-Term Time Series Data in Indonesia
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Implementation of Multiplicative Seasonal ARIMA Modeling and Flood Prediction Based on Long-Term Time Series Data in Indonesia

机译：基于长期时间序列数据在印度尼西亚的长期时间序列数据的实现乘法季节性Arima建模和洪水预测的实施

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摘要

Time series modeling and prediction has fundamental importance in the various practical field. Thus, a lot of productive research works is working in this field for several years. Many essential methods have been proposed in publications to improve the accuracy and efficiency of time series modeling and prediction. This research aims to present the proposed prediction model namely Multiplicative seasonal ARIMA model (MSARIMA) based on non-stationary time series data to predicting the flood event. In this paper, we have described the performance of the ARMA model, the ARIMA model, and MSARIMA model to predict the flood event in the Region over Indonesia in 42 years (1976-2017). We have used the two performance measures respectively (MAPE, and RMSPE) to evaluate prediction accuracy as well as to compare different models fitted to a time series data. The result has shown the proposed predicting model has the best performance accuracy than the others model in this research.
机译：时间序列建模与预测在各种实际领域具有根本重要性。因此，许多生产力研究工作在这一领域工作了几年。在出版物中提出了许多基本方法，以提高时间序列建模和预测的准确性和效率。本研究旨在基于非静止时间序列数据来提出所提出的预测模型即乘法季节性ARIMA模型（MSarima），以预测洪水事件。在本文中，我们已经描述了ARMA模型，ARIMA模型和MSarima模型的表现，以预测42年（1976-2017）的印度尼西亚区域的洪水活动。我们已经分别使用了两种性能测量（MAPE和RMSPE）来评估预测准确性，并比较适配到时间序列数据的不同模型。结果表明，所提出的预测模型具有比本研究中其他模型的性能准确性最佳。

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