Forecasting Time-Series Trends by Merging Structured and Unstructured Datasets

This paper introduces a new approach to forecast daily stock trends by merging structured and unstructured datasets. This study intends to reveal the effectiveness of using supplemental datasets for accurate prediction of stock prices. A set of features, which is seemingly highly correlated with daily stock price variations, are selected using random forest optimization technique. Stock-relevant keywords that are extracted from news articles are converted into a time-series dataset in terms of temporal frequency. Convolution neural network (CNN) based deep learning models are generated separately for stock trading data and keyword frequencies from news articles, and two CNN models are merged together for training input datasets. The analysis results show that merging two different datasets may generate the better forecasting results than using stock trading datasets only. Additional issues for future analysis and implementations are discussed.