论文信息 - Time series modeling and forecasting: Tropical cyclone prediction using ARIMA model

Time series modeling and forecasting: Tropical cyclone prediction using ARIMA model

An Auto Regressive Integrated Moving Average (ARIMA) model is best suited for time series analysis to enhance the analysis of the data and to forecast the unknown data in the series also called forecasting, in Statistical Studies, Analysis, Business Econometrics, Big data and in Predictive analytics, especially for time series analysis,. Tropical Cyclone (TC) is among the most notorious hazard that cost human lives and causes tremendous economic loss, and damages especially to the peoples who reside in the coastal areas. Therefore, it is of considerable importance to correctly predict TC, its tracks and intensities for effective disaster analysis, responses, mitigation and management. This paper presents a Statistical Time Series Modeler (TSM) for prediction of Cyclonic Storm of India. For developing this model, 6 years data (2007-2012) is used, consisting of 14 attributes. TSM of SPSS (Statistical Package for Social Studies) is applied for training and testing the Storm dataset. The model is developed using the training data (2007-2011) and applied using the testing data set (2012). This model is built on ARIMA (Auto Regressive Integrated Moving Average) model of TSM in SPSS 20.0.

G. M. Nasira | A. Geetha

[1] I. Introduction,et al. Time Series Analysis of Forecasting Indian Rainfall , 2013 .

[2] Pankaj Bhambri,et al. Time Series based Temperature Prediction using Back Propagation with Genetic Algorithm Technique , 2011 .

[3] Mark DeMaria,et al. A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin , 1994 .

[4] Wei Zhang,et al. A novel web-based system for tropical cyclone analysis and prediction , 2012, Int. J. Geogr. Inf. Sci..

[5] Andrea Molod,et al. The Use of a Statistical Model of Storm Surge as a Bias Correction for Dynamical Surge Models and its Applicability along the U.S. East Coast , 2015 .

[6] Azuraliza Abu Bakar,et al. Uncertain Time Series in Weather Prediction , 2013 .

[7] Dong Hyun Jeong,et al. Designing a rule-based hourly rainfall prediction model , 2012, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).

[8] A. Geetha,et al. Artificial Neural Networks' Application in Weather Forecasting - Using RapidMiner , 2014 .

[9] Zhiqiang Ma,et al. Hadoop-based ARIMA Algorithm and its Application in Weather Forecast , 2013 .

[10] G. M. Nasira,et al. Data mining for meteorological applications: Decision trees for modeling rainfall prediction , 2014, 2014 IEEE International Conference on Computational Intelligence and Computing Research.