On combining Websensors and DTW distance for kNN Time Series Forecasting

In the pattern recognition field, different approaches have been proposed to improve time series forecasting models. In this sense, k-Nearest-Neighbour (kNN) with DTW (Dynamic Time Warping) distance is one of the most representative methods, due to its effectiveness, simplicity and intuitiveness. The great advantage of the DTW distance is the robustness to distortions in the time axis by allowing stretching and squeezing (time warping) of the time series, while traditional measures require a linear alignment between each data point. However, as well as other traditional measures, the DTW distance has the limitation of focusing only on historical time series data to predict future values, thereby not considering additional external knowledge of the problem domain. In this paper, we propose an approach called TSFW (Time Series Forecasting with Websensors) that incorporates Websensors into DTW distance to improve kNN time series forecasting. Websensors are models that represent knowledge extracted from news about the problem domain as well as the temporal evolution of this knowledge. In our proposed TSFW approach, we show that Websensors allow a more robust non-linear alignment of the time series by using similar events (extracted from news) that have occurred in the both time series. Thus, distortions in the time axis among the time series can be corrected more accurately compared to the traditional technique that uses only the original values of the time series.