Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/250251 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Istanbul Business Research (IBR) [ISSN:] 2630-5488 [Volume:] 50 [Issue:] 1 [Publisher:] Istanbul University Press [Place:] Istanbul [Year:] 2021 [Pages:] 15-46
Verlag: 
Istanbul University Press, Istanbul
Zusammenfassung: 
This study aims to create a monthly sales quantity budget by making use of the previous income data of an enterprise operating within the construction sector, which is considered the locomotive of the economy. For estimating time-series of sales as a linear model ARIMA (Auto-Regressive Integrated Moving Average), as nonlinear model LSTM (Long Shorterm Memory) and a HYBRID (LSTM and ARIMA) model built to improve system performance compared to a single model was used. As a result of the study, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values obtained from each of the methods used in the application were compared, and a monthly sales volume budget was created for 2017 with all the methods used. When the MAPE and MSE values obtained from each of these methods were compared, the best performance was the Hybrid model that gave the lowest error, and in addition, the fact that all of the application models got very realistic results by using the historical data showed the success of the predictions.
Schlagwörter: 
Sales Budget
Time Series Forecast
Hybrid Model
ARIMA
LSTM
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