A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Since we tend to describe a lot of things with a time dimension, we tend to hae more and more of these data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.
Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements. Means that whatever your fields in science you will probably face them. That's why there are a lot of research on different algorithm to deal with them. One field of time series analysis is to predict what happens next. In other words, we want to predict what will occur knowing all the past time you have recorded. Another one is simply to understand what message does the signal contains.
That's why we are interested in these data and try to develop algorithm and essentially application around time series in different fields. Our first applicaton concerns medical purposes seeing as we are working on EEG datas. All our algorithms and tutorials are implementend in Python.
If you have any question, any observation or if you want to join us, to help us, please feel free to contact us.