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Implementation of Multiplicative Seasonal ARIMA Modeling and Flood Prediction Based on Long-Term Time Series Data in Indonesia

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Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11633))

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Abstract

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.

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Acknowledgements

First of all, author thank Prof. Hou Rongtao for his thoroughness, valuable advice, and patience. Also, the author would like to thank Irfan Dwiguna Sumitra who always encourages and inspires with new ideas in our research.

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Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Sri Supatmi & Rongtao Huo

  2. Computer Engineering Department, Universitas Komputer Indonesia, Bandung, West Java, Indonesia

    Sri Supatmi

  3. Postgraduate of Information System Department, Universitas Komputer Indonesia, Bandung, West Java, Indonesia

    Irfan Dwiguna Sumitra

Authors
  1. Sri Supatmi
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  2. Rongtao Huo
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  3. Irfan Dwiguna Sumitra
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Corresponding author

Correspondence to Sri Supatmi .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Supatmi, S., Huo, R., Sumitra, I.D. (2019). Implementation of Multiplicative Seasonal ARIMA Modeling and Flood Prediction Based on Long-Term Time Series Data in Indonesia. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-24265-7_4

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  • Print ISBN: 978-3-030-24264-0

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