Stock market prediction has always caught the attention of many analysts and researchers.
Popular theories suggest that stock markets are essentially a random walk and it is a fool's …

Advanced deep learning methods have been widely adopted in stock movement prediction
with technical analysis (TA), while researchers prefer technical indicators to technical charts …

We ask whether empirical finance market data (Financial Stress Index, swap and equity,
emerging and developed, corporate and government, short and long maturity), with their …

This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify
varying lead-lag relationships between two different time series. Specifically, this manuscript …

Selecting the right features for further data analysis is important in the process of equipment
anomaly detection, especially when the origin data source involves high dimensional data …

The era of information explosion had prompted the accumulation of a tremendous amount of
time-series data, including stationary and nonstationary time-series data. State-of-the-art …

Smart healthcare devices and applications are designed to rely on intelligent sensing
devices and wireless communication networks. The purpose of this integration is to provide …

Time series analysis based on pattern discovery has received a lot of interests in the fields of
economic physics and machine learning due to its simplicity and ability to reveal complex …

This paper presents a new computational finance approach. It combines a grid pattern
recognition technique allied to an evolutionary computation optimization kernel based on …

In recent trends, the stock market prediction has been treated as a challenging task for every
people who have been associated with the financial market. Forecasting is the activity of …