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International Conference on Soft Computing and Data Mining

SCDM 2022: Recent Advances in Soft Computing and Data Mining pp 46–55Cite as

Fuzzy-Autoregressive Integrated Moving Average (F-ARIMA) Model to Improve Temperature Forecast

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Part of the Lecture Notes in Networks and Systems book series (volume 457)

Abstract

A temperature forecast is a form of weather forecast that predicts temperature conditions using science and technology. Temperature forecasting is critical for making decisions in a variety of activities. To attain high predicted accuracy, predictive models must be built using accurate historical data. Data obtained through multiple methods, on the other hand, is inherently unreliable, resulting in less reliable predictive models. Hence, the data must be carefully managed, particularly to remove data uncertainty. While traditional data processing systems are simple to employ, they lack standard approaches for dealing with data uncertainty. As a consequence, this research presents a method for predicting temperature using ARIMA, as well as fuzzy data preparation strategies for dealing with fuzzy data during the data pre-processing phase. Standard deviation approaches were used to build fuzzy triangles for managing fuzzy data. The proposed method for creating fuzzy numbers using standard deviations yields fewer prediction errors and increases model performance, according to the experimental results. This is because data errors have been rectified, and model development errors have been decreased.

Keywords

  • Fuzzy data
  • ARIMA
  • Fuzzy time series
  • Temperature prediction

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Acknowledgments

This research was supported by the Ministry of Education (MOE) through the Fundamental Research Grant Scheme (FRGS/1/2019/ICT02/UTHM/02/7) Vot K208. This research work is also supported by the Ministry of Education, R.O.C., under the grants of TEEP@AsiaPlus. The work of this paper is also supported by the Ministry of Science and Technology under Grant No. MOST 109-2221-E-035-063-MY2.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Muhammad Shukri Che Lah, Nureize Arbaiy & Yana Mazwin Mohmad Hassim

  2. Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Road, Taichung, Taiwan

    Pei-Chun Lin

  3. Fakulti Teknologi Kejuruteraan Elektrik, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

    Shamshul Bahar Yaakob

Authors
  1. Muhammad Shukri Che Lah
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  2. Nureize Arbaiy
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  3. Yana Mazwin Mohmad Hassim
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  4. Pei-Chun Lin
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  5. Shamshul Bahar Yaakob
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Corresponding author

Correspondence to Muhammad Shukri Che Lah .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Mustafa Mat Deris

  4. School of Information Technology Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Prof. Jemal H. Abawajy

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Dr. Nureize Arbaiy

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Lah, M.S.C., Arbaiy, N., Hassim, Y.M.M., Lin, PC., Yaakob, S.B. (2022). Fuzzy-Autoregressive Integrated Moving Average (F-ARIMA) Model to Improve Temperature Forecast. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_5

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