A Novel Time Series Representation Approach for Dimensionality Reduction

With the growth of streaming data from many domains such as transportation, finance, weather, etc, there has been a surge in interest in time series data mining. With this growth and massive amounts of time series data, time series representation has become essential for reducing dimensionality to overcome the available memory constraints. Moreover, time series data mining processes include similarity search and learning of historical data tasks. These tasks require high computation time, which can be reduced by reducing the data dimensionality. This paper proposes a novel time series representation called Adaptive Simulated Annealing Representation (ASAR). ASAR considers the time series representation as an optimization problem with the objective of preserving the time series shape and reducing the dimensionality. ASAR looks for the instances in the raw time series that can represent the local trends and neglect the rest. The Simulated Annealing optimization algorithm is adapted in this paper to fulfill the objective mentioned above. We compare ASAR to three well-known representation approaches from the literature. The experimental results have shown that ASAR achieved the highest reduction in the dimensions. Moreover, it has been shown that using the ASAR representation, the data milling process is accelerated the most. The ASAR has also been tested in terms of preserving the shape and the information of the time series by per forming One Nearest Neighbor (1-NN) classification and K-means clustering, which assures its ability to preserve them by outperforming the competing approaches in the K-means task and achieving close accuracy in the 1-NN classification task.