Comparative Assessment of AR, MA and ARMA for the Time Series Forecasting of Assam and Meghalaya Rainfall Division

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI:10.1109/ComPE49325.2020.9200014

Utpal Barman, Ridip Dev Choudhury, Asif Ekbal Hussain, Mridul Jyoti Dahal, Puja Barman, M. Hazarika

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引用次数: 2

Abstract

Weather Forecasting is a serious issue in agriculture, especially in Assam and Meghalaya. The productivity of agriculture is dependent on rainfall. This paper forwards a comparative assessment of Auto-Regressive, Moving Average, and Auto-Regressive Moving Average Model for rainfall in Assam and Meghalaya. A total of 117 years of rainfall data of Assam and Meghalaya division is collected from data.gov.in [11]. The models are implemented by visualizing the time series components of rainfall. The necessary investigations such as ACF, PACF, rolling mean, and ducky fuller tests are reported in the paper for the analysis of stationarity of time series. The evaluating parameters such regression score (0.73), mean absolute error (75.70), median absolute error (61.43), mean squared error (9396.09), mand root mean square error (96.93) select the ARMA model as the best model for the time series forecasting of Assam and Meghalaya Division.

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阿萨姆邦和梅加拉亚邦降雨分区时间序列预报的AR、MA和ARMA比较评估

天气预报对农业来说是一个严重的问题，尤其是在阿萨姆邦和梅加拉亚邦。农业的生产力依赖于降雨。本文提出了阿萨姆邦和梅加拉亚邦降雨的自回归、移动平均和自回归移动平均模型的比较评估。阿萨姆邦和梅加拉亚邦共117年的降水数据收集自data.gov.in[11]。这些模型是通过可视化降雨的时间序列分量来实现的。本文报道了时间序列平稳性分析所需的ACF、PACF、滚动均值和ducky fuller检验等研究方法。回归分数(0.73)、平均绝对误差(75.70)、中位数绝对误差(61.43)、均方误差(9396.09)和均方根误差(96.93)等评价参数选择ARMA模型为阿萨姆邦和梅加拉亚邦地区时间序列预测的最佳模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 International Conference on Computational Performance Evaluation (ComPE)

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