Transductive LSTM for time-series prediction: An application to weather forecasting

Abstract

Long Short-Term Memory (LSTM) has shown significant performance on many real-world applications due to its ability to capture long-term dependencies. In this paper, we utilize LSTM to obtain a data-driven forecasting model for an application of weather forecasting. Moreover, we propose Transductive LSTM (T-LSTM) which exploits the local information in time-series prediction. In transductive learning, the samples in the test point vicinity are considered to have higher impact on fitting the model. In this study, a quadratic cost function is considered for the regression problem. Localizing the objective function is done by considering a weighted quadratic cost function at which point the samples in the neighborhood of the test point have larger weights. We investigate two weighting schemes based on the cosine similarity between the training samples and the test point. In order to assess the performance of the proposed method in different weather conditions, the experiments are conducted on two different time periods of a year. The results show that T-LSTM results in better performance in the prediction task.

Keywords: Long short-term memory; Transductive learning; Weather forecasting.