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Figure 10 – uploaded by Dr. Amish Soni

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Interpretation: After sequence plotting at first for identification, we have to check whether given data

Figure 10 Interpretation: After sequence plotting at first for identification, we have to check whether given data

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Autocorrelations
Autocorrelations
Table 4: Case Processing Summary
Table 4: Case Processing Summary
lata is non — Stationary. Hence to make it Stationary Arima Model Is required. 4.1.4 Identification Graph- ACF
lata is non — Stationary. Hence to make it Stationary Arima Model Is required. 4.1.4 Identification Graph- ACF
Series: Price
Series: Price
Number of Computable First Lags After Differencins
Number of Computable First Lags After Differencins
Case Processing Summary Series: Price
Case Processing Summary Series: Price
Series: Price Table 9 Partial Autocorrelations
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
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
Model Description
The best model for data is the one with lowest BIC Value. ‘good’ BIC value. BIC value needs to be compared
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
Applying the model specifications from MOD_1 4.2 Forecast analysis of BSE Index Price
Case Processing Summary
Case Processing Summary
& 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
& 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
Case Processing Summary 4.2.4 BSE Index Price
Series: bse index price
Series: bse index price
BIC value is lower from hence it predicts that this
BIC value is lower from hence it predicts that this
low correlation between variables.
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
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
ARIMA Model Parameters
For each model, forecasts start after the last non-missing in the range of the requested estimation period Forecast
For each model, forecasts start after the last non-missing in the range of the requested estimation period Forecast
applying ARIMA model we get
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.
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
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.
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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