Special Issue on Time Series Analysis

Time Series Analysis is a statistical method of dealing with dynamic data, which is based on random process theories and mathematical statistical methods. Time series analysis establishes models through the curve fitting and parameter estimation, to explore the statistical characteristics of previously observed data and predict future values. Methods of time series analysis may be divided into frequency-domain and time-domain methods, parametric and non-parametric methods, linear and non-linear methods, univariate and multivariate methods. As a statistical forecast tool to solve practical problems, it’s used in statistics, econometrics, mathematical finance, weather forecasting, astronomy and any domain involves temporal measurements. The goal of this special issue is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in the area of research on the time series analysis.

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on exploring time series analysis. Potential topics include, but are not limited to:

• Quantitative forecasting
• Component decomposition analysis
• ARIMA models
• GARCH models
• Vector autoregression
• Fourier analysis
• Non-linear models

Authors should read over the journal’s For Authors carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

Please kindly specify the “Special Issue” under your manuscript title. The research field “Special Issue - Time Series Analysis” should be selected during your submission.

According to the following timetable:

Guest Editor:

For further questions or inquiries

Please contact Editorial Assistant at

ojs@scirp.org