Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN

被引：62

作者：

Chattopadhyay, Surajit ^{[1
]}

Chattopadhyay, Goutami ^{[2
]}

机构：

[1] Pailan Coll Management & Technol, Dept Comp Applicat, Kolkata 700104, India

[2] Univ Calcutta, Dept Atmospher Sci, Kolkata 700019, India

来源：

COMPTES RENDUS GEOSCIENCE | 2010年 / 342卷 / 02期

关键词：

Univariate model; Stationarity; ARIMA; ARNN; Multilayer perceptron; Rainfall; ARTIFICIAL NEURAL-NETWORKS; TOTAL OZONE; INTERANNUAL VARIABILITY; NEUROCOMPUTING APPROACH; STOCHASTIC-MODELS; PREDICTION; PRECIPITATION; INDIA; CHAOS; GENERATION;

D O I：

10.1016/j.crte.2009.10.016

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The present article reports studies to develop a univariate model to forecast the summer monsoon (June-August) rainfall over India. Based on the data pertaining to the period 1871-1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described. (C) 2009 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.

引用

页码：100 / 107

页数：8

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