Recurrent neural networks have received vast amount of attention in time series prediction due to their flexibility in capturing dependencies on various scales. However, as in most of the classical forecasting methods, its accuracy is strongly tied to the degree of signal complexity. Specifically, stock market prices are commonly classified to be non-linear, non-stationary and chaotic signals, since they exhibit erratic behavior that conducts a poor performance in the long short-term memory (LSTM). In this paper, we propose a methodology to improve the predictability of financial time series by using the complete ensemble empirical mode decomposition with adaptive noise and the intrinsic sample entropy (SampEn). We evaluated the integrated model by applying it to S&P 500 index stocks for the period between January 2018 and April 2020 and for each time series of stock closing prices, an LSTM model was trained to forecast the next closing price. The experimental results represent a dependency between the decomposed signal entropy and the performance of forecast accuracy. This suggests that in those cases where the short-term complexity in financial time series is smaller compared to the series energy, the forecasting capabilities are significantly improved after the removal of decomposed highest frequency. Furthermore, our results show an improvement in forecasting the direction of the stock price by 31% using the classical LSTM architecture.

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The existence of well-water contaminants can impact negatively on human health when ingested for drinking water due to the deficient information about these pollutants in San Bernardino County. The San Bernardino County Department of Public Health is dedicated to protecting its residents’ health by providing a look-up table system, which contains general information about harmful pollutants. This project report explores the use of geographic information systems (GIS) to significantly enhance the water well look up table system for conveying timely and accurate information to end users. This GIS framework features a cloud-based system that encompasses interactive web maps and metrics dashboard, which are important for location-based data visualization. The research delivers new insights into how GIS technology can effectively convey comprehensive well-water pollutant information, and enhance the data management system, so users can make informed decisions for well-water consumption.