Personal Loan Sales Forecasting Through Time Series Analysis
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Keywords

Time Series, Sales, Correlogram.

How to Cite

ÖZEROĞLU, A. İhsan. (2021). Personal Loan Sales Forecasting Through Time Series Analysis. PRIZREN SOCIAL SCIENCE JOURNAL, 5(1), 44-51. https://doi.org/10.32936/pssj.v5i1.216

Abstract

Almost all state enterprises and private sector companies try to foresee future expectations. From the viewpoint of economic, productive, and efficient business management, this is highly important. By making rational decisions, all enterprises aim to rich maximum profitability by taking sales, cost, human resource needs, profits into account. For this reason, enterprises have to make reliable and reasonable forecasts to take the right decisions. 

Such forecasts might be used in budgeting, cost, and profit analysis. Forecasted scenarios might come true in the future with a great likelihood. The researcher utilizing time series analysis assumes that all findings that come out will be almost the same happened in the past.

Analyzing the time series consist of four aims such as defining, modeling forecasting, and controlling. To define a series, it is needed to compute definitional statistics and to draw its graphic. The second purpose of analyzing the time series is to find the appropriate model of the time series.

With that work called “Time series and application to sale data”, it is tried to make a suitable guess model by analyzing the data of personal loans of a bank 2004-2010 sale data based on unit. During the stagnation stage of the sequence correlogram and root, analyses are performed. The sequence is analyzed with the help of the Eviews 5,1 program. At the end of the survey, it is seen that natural logarithmic personal loan sale sequences are at their level and in the first gap it is not constant and it is also seen that when the second gap is taken, the constant is obtained. The sequence of which the second gap is taken is shown based on time-way graphs and correlogram. When the constant is provided, the guessed model is formed by taking the second gap. The suitability of the model is observed by the correlogram, Akaike information criteria (AIC), and Schwarz information criteria (SIC) merits.

https://doi.org/10.32936/pssj.v5i1.216
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