Cover page and Table of Contents: PDF (size: 667KB)
Full Text (PDF, 667KB), PP.57-63
Views: 0 Downloads: 0
ARIMA (Autoregressive Integrated Moving Average), ETS (Exponential Smoothing), AIC (Akaike’s Information Criteria), and BIC (Bayesian Information Criteria), RMSE (Root Mean Square Error)
The aim of the study is to introduce some appropriate approaches which might help in improving the efficiency of weather’s parameters. Weather is a natural phenomenon for which forecasting is a great challenge today. Weather parameters such as Rainfall, Relative Humidity , Wind Speed , Air Temperature are highly non-linear and complex phenomena, which include statistical simulation and modeling for its correct forecasting. Weather Forecasting is used to simplify the purpose of knowledge and tools which are used for the state of atmosphere at a given place. The expectations are becoming more complicated due to changing weather state. There are different software and their types are available for Time Series forecasting. Our aim is to analyze the parameter and do the comparison of some strategies in predicting these temperatures. Here we tend to analyze the data of given parameters and to notice their predictions for a particular period by using the strategy of Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS) .The data from meteorological centers has been taken for the comparison of methods using packages such as ggplot2, forecast, time Date in R and automatic prediction strategies which are available within the package applied for modeling with ARIMA and ETS methods. On the basis of accuracy we tend to attempt the simplest methodology and then we will compare our model on the basis of MAE, MASE, MAPE and RMSE. An identification of model will be the chromatic checkup of both the ACF and PACF to hypothesize many probable models which are going to be projected by selection criteria i.e. AIC, AICc and BIC.
Er. Garima Jain, Bhawna Mallick,"A Study of Time Series Models ARIMA and ETS", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.4, pp.57-63, 2017. DOI:10.5815/ijmecs.2017.04.07
Agrawal , R. Jain, R.C. Jha, M.P. and Singh, “Forecasting of rice yield using climatic variables”, Indian Journal of Agricultural Science, Vol. 50, No. 9, pp. 680-684, 1980.
G.E.P. Box, G. Jenkins, “Time Series Analysis, Forecasting and Control”, Holden-Day,San Francisco, CA, 1970.
Akaike, H. (1976), “An information criterion (AIC)”, Math Sci. 14, 5-9.
Hyndman, R. J., & Khandakar, " Automatic time series for forecasting: the forecast Package for R (No. 6/07)”, Monash University of Department of Econometrics and Business Statistics, 2007.
Tektas M. Weather forecasting using ANFIS and ARIMA, “A case study for Istanbul. Environmental Research, Engi-neering and Management”, 2010; 1(51):5–10.
G.Vamsi Krishna (2014), “An Integrated Approach for Weather Forecasting based on Data Mining and Forecasting Analysis”, Indian Journal of Computer Science and Engineering (IJCSE) by Vol. 5 No.2 .
Agrawal et al., Ratnadip Adhikari, R. K. Agrawal, “An Introductory Study on Time Series Modeling and Forecasting”.
Pinky Saikia Dutta et.al, Hitesh Tahbilder, “Prediction of Rainfall using DataMinning Technique over Assam”, Indian Journal of Computer Science and Engineering (IJCSE), 2014.
Mahmudur Rahman, A.H.M. Saiful Islam , Sahah Yaser Maqnoon Nadvi , Rashedur M Rahman , “Comparative Study of ANFIS and ARIMA Model for weather forecasting in Dhaka” ,IEEE, 2013.
Dedetemo Kimilita Patrick1, Phuku Phuati Edmond2, Tshitenge Mbwebwe Jean-Marie2, Efoto Eale Louis2, Koto-te-Nyiwa Ngbolua, “Prediction of rainfall using autoregressive integrated moving average model”, Case of Kinshasa city (Democratic Republic of the Congo), from the period of 1970 to 2009 Volume 2/ Issue 1 ISSN: 2348 – 7321, 2014.
Akaike, H., “A New Look at the Statistical Model Identification”, IEEE Transaction on Automatic Control, 19,716-723, 1974.
Garima Jain, Bhawna Mallick , “A Review on Weather Forecasting Techniques”, Vol. 5, Issue 12, 2016.
Parihar, Jai Singh, Markand P. Oza, Jai S.Parihar, Genya Saito. “Anintegrated approach for crop assessment and production forecasting”, Agriculture and Hydrology Applications of Remote Sensing, 2006.
Robert H. Shumway, “ARIMA Models”, Springer Texts in Statistics, 2011.
Shabri, Ani Samsudin, Ruhaidah., “Fishery landing forecasting using wavelet-based autoregressive integrated moving average models..Res”,, Mathematical Problems in Engineering, Annual 2015 Issue.
Srikanth, P., D. Rajeswara Rao, and P. Vidyullatha,“Comparative Analysis of ANFIS, ARIMA and Polynomial Curve Fitting for Weather Forecasting”, Indian Journal of Science and Technology, 2016.
Shaminder Singh, Jasmeen Gill,“Temporal Weather Prediction using Back Propagation based Genetic Algorithm Technique”, International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.12, pp.55-61, 2014. DOI: 10.5815/ijisa.2014.12.08.