Personal Loan Sales Forecasting Through Time Series Analysis


Time Series, Sales, Correlogram.

How to Cite

ÖZEROÄžLU, A. Ä°hsan. (2021). Personal Loan Sales Forecasting Through Time Series Analysis. PRIZREN SOCIAL SCIENCE JOURNAL, 5(1), 44–51.


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 dataâ€, 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.


Akdi, Y. (2003). Time Series Analysis (Unit Roots and Co-integration). Ankara. Bıçaklar Kitabevi. p. 11. Available at:

Box, G. P. E. & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. San Francisco. Holden-Day.

Chatfield, Ch. (1995). The Analysis of Time Series: An Introduction. Newyork. Chapman and Hall.

Cochrane, J. H. (1997). Time Series for Macroeconomics and Finance. USA. Available at:

Creel, M. (2005). Econometrics. Dept. Of Economics and Economic History. Universitat Autonoma de Barcelona. p. 130.

Creel, M. (2005). Econometrics. Barcelona. Dept. Of Economics and Economic History. Barcelona.

Damodar, N. G. (2004). Basic Econometrics. Newyork. The McGraw- Hill Companies. p. 23.

Delurgio, S.A. (1998). Forecasting Principles. and Applications. Newyork. Irwing McGraw-Hill Comp. p. 59-60.

Griffths, W.E., Hill, R. C., & Judge, G.G. (1993). Learning and Practicing Econometrics. New York. John Wiley & Sons. p. 642.

Ä°slamoÄŸlu, A.H. (2008). Marketing Management. Ä°stanbul. Beta Publications.

Johnston, J. & Dinardo, J. (1997). Econometric Methods. New York. McGraw-Hill International Edit.

Kutlar, A. (2005). Applied Econometrics. Ankara. Nobel Yayın Dağıtım.

Maddala, G. S., & Kim, I. M. (1998). Unit Root Co-integration and Structural Change. Cambridge. Cambridge University Press.

Maddala, G. S. (1992). Introduction to Econometrics. New York. Macmillan Publishing Company. Available at:

Patterson, K. (2000). An Introduction to Applied Econometrics: A Time Series Approach. New York. Great Britain.

Pindyck, R. S. & Rubinfeld, D.L. (1998). Singapore Econometric Models and Economic Forecasts. Irwin/ McGraw-Hill International Edit.

Pollock, D. S. G. (1999). A Handbook of Time Series Analysis Signal Processing and Dynamics. USA. Academic Press.

Pollock, D. S. G. (1999). A Handbook of Time Series Analysis Signal Processing and Dynamics. USA. Academic Press.

Serper, Ö. (1996). Applied Statistics 2. Istanbul. Filiz Publishing House. p. 289.

Sevüktekin M. & Nargeleçekenler M. (2005). Time Series Analysis. Ankara. Nobel Yayın Dağıtım.

Sucu, Y. (2000). Foresight and Forecasting Methods in Management. Un-updated Book.

Tari, R. (2005). Econometrics. Istanbul. Avcı Offset. p. 429-430.

Tsay, R. S. (2004). Analysis of Financial Time Series. USA. John Wiley & Sons.

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Copyright (c) 2021 Ali İhsan ÖZEROĞLU


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