Research indicates that forecast combination is one of the most important and effective
approaches for time series forecasting. The success of forecast combination depends on …

Online data visualizations play an important role in informing public opinion but are often
inaccessible to screen reader users. To address the need for accessible data …

Temporal data is one of the most common form of data in internet of things. Data from
various sources such as sensors, smart phones, smart homes and smart vehicles in near …

Mining temporal association patterns from time-stamped temporal databases, first
introduced in 2009, remain an active area of research. A pattern is temporally similar when it …

This article proposes a novel fuzzy recommendation system for stock market investors. This
intelligent decision tool uses fuzzy Japanese candlesticks and includes the effect of currency …

Most forecasting algorithms in financial markets use physical time for studying price
movements, making the flow of time discontinuous. The use of physical time scale can make …

In this paper, we propose an efficient segmentation approach in order to divide a
multivariate time series through integrating principal component analysis (PCA), visibility …

Currently, the world economics develops rapidly, and the finance business also develops
promptly. As there are more financial activities, the uncertainty of change trend in financial …

Directly mining high-dimensional time series presents several challenges, such as time and
space costs. This study proposes a new approach for representing time series data and …

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 …