Feature selection of weather data with interval principal component analysis

Principal Component Analysis (PCA) has long been used as a tool in exploratory data analysis, making predictive models and reducing the size of data. However, many situations in which the use of single-valued variable may cause loss of information. In this paper, an interval PCA is proposed to find out the feature with interval weather data under the season. Besides, interval PCA reveal underlying structure, as well as expose the inside variation of the observations. That is, using weather datasets with different length of time (e.g., month, season) are presented to show each patterns. Finally, the compared results between single-valued and interval-valued weather data illustrates that interval-valued data analysis can display more information.