MULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL
Time series modeling is a model used to analyze data by considering the effect of data on previous periods. The Vector Autoregressive (VAR) model is a multivariate time series model that can explain the interdependency relationship between several variables. The VAR model is the generalization resul...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Final Project |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/51313 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Institut Teknologi Bandung |
| 語言: | Indonesia |
| 總結: | Time series modeling is a model used to analyze data by considering the effect of data on previous periods. The Vector Autoregressive (VAR) model is a multivariate time series model that can explain the interdependency relationship between several variables. The VAR model is the generalization result of the univariate time series model, namely the Autoregression (AR) model by allowing more than one stochastic process variable to be created. In this final project, describes how to model a stable VAR model in order to get the best model. The results of the case studies show that the VAR model is more suitable for use with original stationary data and the number of variables in the data does not affect the performance of the VAR model. The VAR model can be used on discrete data that has outliers or outliers. |
|---|