Classical and bayesian statistical methods for demand forecasting. A comparative analysis
Comparisons between forecast models are necessary for decision making in industry, especially for demand prediction. In the presence of few historical data, there could be diffculties in the compliance of theoretical premises. In this paper, a comparison is presented designed in R program, using four types of models: Bayesian linear regression with normal prior distribution, bayesian dynamic linear model, ARIMA and exponential smoothing, based on criteria: Mean Absolute Percentage Error (MAPE) of forecasts, and therefore different data scenarios are simulated, reflecting demand behavior with and without Normal Distribution and with or without dynamic variance. Bayesian models under study were found to have a high potential in predictions, especially for data that does not behave with a normal distribution, being more precise than the other classical models compared, besides, they are more robust to theoretical premises, and they can be used with few historical data.
| Main Authors: | , , |
|---|---|
| Format: | Digital revista |
| Language: | spa |
| Published: |
Universidad Nacional de Colombia - Sede Medellín - Facultad de Ciencias
2015
|
| Online Access: | https://revistas.unal.edu.co/index.php/rfc/article/view/49775 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|