AQI Prediction Based on CEEMDAN-ARMA-LSTM

On stock return prediction with LSTM networks

Artificial neural networks are, again, on the rise. The decreasing costs of computing power and the availability of big data together with advancements of neural network theory have made this possible. In this thesis, LSTM (long short-term memory) recurrent neural networks are used in order to perform financial time series forecasting on return data of three stock indices. The indices are S&P 5...

House Price Prediction Using LSTM

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squar...

Effect of PM2.5 on AQI in Taiwan

US EPA has included sub-indices of 8-hour average O3 concentration and 24-hour average PM2.5 level into a newly-modified Air Quality Index (AQI). Calculations at air-quality monitoring stations in Taiwan show that the updated AQI system is associated with two to three times higher occurring frequency of days with AQI larger than 100 than that of the widely used Pollution Standard Index (PSI) la...

Kernel smoothed prediction intervals for ARMA models

Kernel smoothed prediction intervals for ARMA models

Abstract The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical forecast errors using lead t...