Autocorrelations Table 4: Case Processing Summary lata is non — Stationary. Hence to make it Stationary Arima Model Is required. 4.1.4 Identification Graph- ACF Series: Price Number of Computable First Lags After Differencins Case Processing Summary Series: Price Series: Price Table 9 Partial Autocorrelations 4.1.10 Stationary data: When spikes of both ACF &PACF is within confidence level is known as stationary data. So, at first for Model Description The best model for data is the one with lowest BIC Value. ‘good’ BIC value. BIC value needs to be compared Applying the model specifications from MOD_1 4.2 Forecast analysis of BSE Index Price & chart where it discloses model name, which series is used in model on which ARIMA model is focused The first stage in the ARIMA Model is to determine if the data is stationary or non-stationary. We may use Case Processing Summary 4.2.4 BSE Index Price Series: bse index price Interpretation: After sequence plotting at first for identification, we have to check whether given data BIC value is lower from hence it predicts that this low correlation between variables. Interpretation: By taking difference 1 model. we can conclude that NOT all lags respectively for Residua ACF & Residual PACF falls in 95% confidence level. Between UCL & LCL but at level 1, minimum lag: touch the UCL /LCL, so we can conclude our model is fit to run with appropriate forecast values. fa nee oe oe, +o ARIMA Model Parameters For each model, forecasts start after the last non-missing in the range of the requested estimation period Forecast applying ARIMA model we get Regression analysis between BSE Index of oil & gas industries & Crude oil prices Fall of prices this is due to Corona emerging situation along with other external factors. b= slope of regression line (which is rate of change for y as x changes So, in our case we can say there is strong positive relationship between index &crude oil prices i.e — 0.94 5. Findings Percentile value discloses probability output which is shown with help of graph. residual helps us to predict how far actual forecast is there from estimated forecast. There is an increase in price of crude oil on basis of estimate value.
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