Weather Forecasting Using Merged Long Short-term Memory Model

Short-Term Weather Forecasting

Time Series Forecasting Based on Augmented Long Short-Term Memory

In this paper, we use variational recurrent model to investigate the time series forecasting problem. Combining recurrent neural network (RNN) and variational inference (VI), this model has both deterministic hidden states and stochastic latent variables while previous RNN methods only consider deterministic states. Based on comprehensive experiments, we show that the proposed methods significa...

Short term gust forecasting by using numerical weather prediction and multi timescale nonparametric regression model

In this study, an ARX model with nonparametric multi time scale regression model was proposed for short term gust forecasting and applied to a ski resort in Japan. Proposed ARX model can be used for short term forecasting of mean and fluctuating wind speed. Proposed nonparametric multi time scale regression model can appropriately estimate the peak factor and shows better agreement with the mea...

Enhance the performance of weather parameters in Short-Term Weather forecasting using ANFIS

Weather prediction is an ever challenging area of investigation for scientists. The Adaptive Neuro-Fuzzy Inference System (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different ki...

Speech dereverberation using long short-term memory

Recently, neural networks have been used for not only phone recognition but also denoising and dereverberation. However, the conventional denoising deep autoencoder (DAE) based on the feed-forward structure is not capable of handling very long speech frames of reverberation. LSTM can be effectively trained to reduce the average error between the enhanced signal and the original clean signal by ...