Fuzzy-Autoregressive Integrated Moving Average (F-ARIMA) Model to Improve Temperature Forecast-Reference-Cited by-同舟云学术

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

Published:2022 Issue: Volume: Page:46-55
ISSN:2367-3370
Container-title:Recent Advances in Soft Computing and Data Mining
language:
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Author:

Lah Muhammad Shukri Che,Arbaiy Nureize,Hassim Yana Mazwin Mohmad,Lin Pei-Chun,Yaakob Shamshul Bahar

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-00828-3_5

Reference23 articles.

1. Alley, R.B., Emanuel, K.A., Zhang, F.: Advances in weather prediction. Science 363(6425), 342–344 (2019)

2. Powers, J.G., et al.: The weather research and forecasting model: overview, system efforts, and future directions. Bull. Am. Meteorol. Soc. 98(8), 1717–1737 (2017)

3. Steinker, S., Hoberg, K., Thonemann, U.W.: The value of weather information for e-commerce operations. Prod. Oper. Manag. 26(10), 1854–1874 (2017)

4. Karevan, Z., Mehrkanoon, S., Suykens, J.A.: Black-box modeling for temperature prediction in weather forecasting. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2015)

5. Bendre, M.R., Thool, R.C., Thool, V.R.: Big data in precision agriculture: weather forecasting for future farming. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 744–750. IEEE (2015)

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1. Weather Prediction Platform Using Data Mining;2024 6th International Conference on Computing and Informatics (ICCI);2024-03-06

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