Weighted feature-based classification of time series data

Classification is one of the most popular techniques in the data mining area. In supervised learning, a new pattern is assigned a class label based on a training set whose class labels are already known. This paper proposes a novel classification algorithm for time series data. In our algorithm, we use four parameters and based on their significance on different benchmark datasets, we have assigned the weights using simulated annealing process. We have taken the combination of these parameters as a performance metric to find the accuracy and time complexity. We have experimented with 6 benchmark datasets and results shows that our novel algorithm is computationally fast and accurate in several cases when compared with 1NN classifier.