The prediction of the future has fascinated the human being since its early existence. Actually, many of these efforts can be noticed in everyday events such as energy management, telecommunications, pollution, bioinformatics, seismology, and, obviously, neuroscience. Accurate predictions are essential in economic activities as remarkable forecasting errors in certain areas may involve large loss of money.

Given this situation, the successful analysis of temporal data has been a challenging task for many researchers during the last decades and, indeed, it is difficult to figure out any scientific branch with no time-dependent variables.

Computational intelligence is known for including powerful techniques like artificial neural networks, fuzzy systems, evolutionary computation, learning theory, or probabilistic methods. Thus, this special issue is focused to the application of such techniques to time series.

The goal of this special issue is to share recent advances in time series analysis and to provide an interesting opportunity to present and discuss the latest practical advanced in real-world applications.

In this sense, original works in the field of both classification and forecasting are welcome. Results with application to neuroscience are particularly encouraged.

Potential topics include, but are not limited to:

• Neural models in time series analysis
• Deep learning in time series analysis
• Soft computing in time series analysis
• Bioinspired models in time series analysis
• Fuzzy systems in time series analysis
• Probabilistic methods in time series analysis
• All of the above-mentioned techniques in the big time series data context