Sequential discovery from time series data has mainly concerned about events or item sets. Recently, the research has stated to applied to the numerical data. An example is sensor information generated by checking a machine state. The numerical data hardly have the same valuers while making patterns. So, it is important to extract suitable number of pattern features, which can be transformed to events or item sets and be applied to sequential pattern mining tasks. The popular methods to extract the patterns are sliding window and clustering. The results of these methods are sensitive to window sine or clustering parameters; that makes users to apply data mining task repeatedly and to interpret the results. This paper suggests the method to retrieve pattern features making numerical data into vector of an angle and a magnitude. The retrieved pattern features using this method make the result easy to understand and sequential patterns finding fast. We define an inclusion relation among pattern features using angles and magnitudes of vectors. Using this relation, we can fad sequential patterns faster than other methods, which use all data by reducing the data size.