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

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.

Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather Forecasting.

Entropy measures have been a major interest of researchers to measure the information content of a dynamical system. One of the well-known methodologies is sample entropy, which is a model-free approach and can be deployed to measure the information transfer in time series. Sample entropy is based on the conditional entropy where a major concern is the number of past delays in the conditional term.