AI Summary & Key Takeaways
AI InsightSatisfactory results have been obtained using the selected ARIMA models,suggesting that a simplistic modeling technique for determining the future values of AOD is feasible.
📌 3 Key Takeaways
Özet
The present study focuses on the application of stochastic modeling technique in analyzing the futuretrends of aerosol optical properties. For this, the BoxeJenkins ARIMA (Autoregressive Integrated MovingAverage) model has been used for simulating the monthly average Aerosol Optical Depth (AOD550 nm)retrieved from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) over New Delhi, theurban capital of India. The satellite dataset has been collected for a period of ten years from 2004 to 2014.The analysis of autocorrelation function indicates existence of seasonality in the AOD time series. Severalseasonal ARIMA models have been generated and their validation has been verified by assessing variousestimation parameters, using the Statistical Package for the Social Sciences (SPSS, version 20). Afterrigorous evaluation of the selected models, the ARIMA (1,0,0)x(0,1,2)12 is identified as the best fit modelw.r.t. measures of goodness-of-fit like Stationary R-square (0.530), R-square (0.674), Root Mean SquaredError (0.128); Mean Absolute Error (0.095); Mean Absolute Percentage Error (16.942); and normalizedBayesian Information Criteria (3.941). The selected models have been further used to forecast AODvalues for the year 2014 at 95% level of confidence. However, the ARIMA (1,0,0)x(2,1,1)12 model is foundto have minimum forecasting error, calculated as Mean Percentage Error (0.220). As the difference in BICof both the models is minimal (0.046), so both the models have been considered as best fit models andutilized for prediction of AOD. Satisfactory results have been obtained using the selected ARIMA models,suggesting that a simplistic modeling technique for determining the future values of AOD is feasible.