Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/297618 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Contemporary Economics [ISSN:] 2300-8814 [Volume:] 17 [Issue:] 1 [Year:] 2023 [Pages:] 10-23
Verlag: 
University of Finance and Management in Warsaw, Faculty of Management and Finance, Warsaw
Zusammenfassung: 
Firms selling commercial vehicles often face difficulties due to recessions in the globalized economy. Manufacturers are keen to anticipate demand in future quarters to optimize their production schedules. In this study, commercial vehicle production data from a leading Indian automotive manufacturer were analyzed us- ing moving averages, exponential smoothing, seasonal decomposition and autoregressive integrated moving average (ARIMA) models with the goal of forecasting. The results reveal that the ARIMA (0,1,1) model effectively predicts the sectoral downturn coinciding with the global financial crisis of 2008. As life returns to normal after the financial crisis caused by COVID-19, such models may be used to strategically move past the disruption.
Schlagwörter: 
demand forecasting
Python programming language
seasonal decomposition
Box-Jenkins methodology
JEL: 
C22
B23
G01
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