Beta autoregressive moving average model selection with application to modeling and forecasting stored hydroelectric energy

2021 ◽  
Author(s):  
Francisco Cribari-Neto ◽  
Vinícius T. Scher ◽  
Fábio M. Bayer
Keyword(s):  
Model Selection ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Hydroelectric Energy ◽  
Autoregressive Moving Average Model ◽  
Modeling And Forecasting ◽  
Moving Average Model

Modeling and Forecasting Sales Data by Time Series Analysis

1981 ◽  
Vol 18 (1) ◽  
pp. 94-100 ◽  
Author(s):  
S. G. Kapoor ◽  
P. Madhok ◽  
S. M. Wu
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Time Series Modeling ◽  
Average Model ◽  
Sales Data ◽  
Autoregressive Moving Average Model ◽  
Deterministic Function ◽  
Modeling And Forecasting ◽  
Moving Average Model

Time series modeling technique is used to model a series of sales data in which seasonality causes distinct spike peaks. The analysis of actual sales data shows that the seasonality in the data can be approximated by a deterministic function and the stochastic component is a sixth-order autoregressive moving average model. Use of the combined deterministic and stochastic models to derive the minimum mean squared forecast yields reliable results.

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