The comparative forecast performance of univariate and multivariate model: an application to time series forecasting
Time series forecasting is a major challenge in many real world applications such as stock price analysis, electricity prices, natural rubber prices and flood forecasting. This type of forecasting is to predict the values of a continuous variable (called as response variable or output variable) with...
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| தலைமை எழà¯à®¤à¯à®¤à®¾à®³à®°à¯à®•à®³à¯: | , , |
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| வடிவமà¯: | Thesis |
| வெளியீடபà¯à®ªà®Ÿà¯à®Ÿà®¤à¯: |
2009
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| பகà¯à®¤à®¿à®•à®³à¯: | |
| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•à®²à¯: | http://eprints.utm.my/18380/ |
| கà¯à®±à®¿à®¯à¯€à®Ÿà¯à®•à®³à¯: |
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| தொகà¯à®ªà¯à®ªà¯: | Time series forecasting is a major challenge in many real world applications such as stock price analysis, electricity prices, natural rubber prices and flood forecasting. This type of forecasting is to predict the values of a continuous variable (called as response variable or output variable) with a forecasting model based on historical data. There are two types of time series forecasting modeling methods; univariate and multivariate. |
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