Impact of time series prediction to the online booking system (internet) on the libraries employing Poisson logarithmic linear model

Abstract

In this study, the effect on the series prediction of the financial system of the central library has been investigated and analyzed accordingly. Four models have been conducted to analyze the series prediction of the library as well as to investigate the monthly income. These models included the Seasonal indexing model (SIM) and Prediction of ARIMA model (PARIMA). Furthermore, Poisson logarithmic linear model has been applied for all suggested models accordingly. The results based on the given models have been verified based on Heteroskedasticity Test. Six months have been included beginning with Jan and ending with Jun. According to the statistical analysis, the verification method used the Heteroskedasticity test. The results revealed that the three models have been verified and were ready to be employed in the next step of the procedure. The PARIMA model has a maximum R2 of 2.7. A certain effective model was employed to predict time series for the used period (Jan to Jun). At these indexations, the lag value has reached a maximum of 0.98. In April, the correlation reached 0.344. Seasonal indexation values for the chosen time have been explained (six months). The figures shifted from month to month. According to the investigation, the highest degree of indexation occurred in April and the lowest level occurred in June. The linear Poisson logarithmic distribution has been explored and examined. At the SIM model, the standard error was reported within the maximum level of 0.3. From the beginning of the year through the end of the year, six months have been documented (X1 to X6). The month of March was the most deviant. In January, the residual Dif has achieved its greatest value of 0.092