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...
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id-itb.:513132020-09-28T10:38:18ZMULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL Rizki Maulana, Muhammad Indonesia Final Project multivariate time series regression, VAR model, VAR model stability, predictive ability. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/51313 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. text |
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Indonesia Indonesia |
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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. |
| format |
Final Project |
| author |
Rizki Maulana, Muhammad |
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Rizki Maulana, Muhammad MULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL |
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Rizki Maulana, Muhammad |
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Rizki Maulana, Muhammad |
| title |
MULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL |
| title_short |
MULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL |
| title_full |
MULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL |
| title_fullStr |
MULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL |
| title_full_unstemmed |
MULTIVARIATE TIME SERIES MODELING ON COVID-19 DATA USING VECTOR AUTOREGRESSIVE MODEL |
| title_sort |
multivariate time series modeling on covid-19 data using vector autoregressive model |
| url |
https://digilib.itb.ac.id/gdl/view/51313 |
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1822000917824667648 |