Applying a Multi-dimensional Time-Series Similarity Method to Typhoon-track Prediction

A tropical cyclone is one of the most threatening natural phenomena and can result in great human and economic loss. To reduce the damage and protect people´s lives, it is becoming increasingly important to predict the movement or track of a typhoon. Although there are several methods of predicting a typhoon track, the results are not sufficiently accurate to utilize when a typhoon is threatening a country or area. To reduce the prediction error, in this paper a multi-dimensional time series similarity method called Modified A-LTK, Approximation with use of Local features at Thinned-out Keypoints, is applied to the prediction. Our preliminary evaluation indicates that the error between the original data and the predicted data was reduced using Modified A-LTK compared with other existing methods such as DTW and AMSS.