scholarly journals MODELING AND FORECASTING BY THE VECTOR AUTOREGRESSIVE MOVING AVERAGE MODEL FOR EXPORT OF COAL AND OIL DATA (CASE STUDY FROM INDONESIA OVER THE YEARS 2002-2017)

2019 ◽  
Vol 9 (4) ◽  
pp. 240-247
Author(s):  
Warsono Warsono ◽  
Edwin Russel ◽  
Wamiliana Wamiliana ◽  
Widiarti Widiarti ◽  
Mustofa Usman
Keyword(s):  
Moving Average ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Modeling And Forecasting ◽  
Moving Average Model ◽  
Vector Autoregressive Moving Average

Tracy–Widom law for the largest eigenvalue of sample covariance matrix generated by VARMA

2020 ◽  
pp. 2150022
Author(s):  
Boping Tian ◽  
Yangchun Zhang ◽  
Wang Zhou
Keyword(s):  
Covariance Matrix ◽  
Moving Average ◽  
Sample Covariance Matrix ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Largest Eigenvalue ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Sample Covariance ◽  
Moving Average Model

In this paper, we derive the Tracy–Widom law for the largest eigenvalue of sample covariance matrix generated by the vector autoregressive moving average model when the dimension is comparable to the sample size. This result is applied to make inference on the vector autoregressive moving average model. Simulations are conducted to demonstrate the finite sample performance of our inference.

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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