Project CEMAPRE internal
| Title | Classification and clustering of big data time series with spectral measures |
| Participants | Jorge Caiado, Nuno Crato (Principal Investigator) |
| Summary | The big data revolution is now offering researchers and analysts new possibilities and new challenges. This is particularly true with time series, as for many domains we now have access to very long time series and to many time series related to a given domain of interest. This happens in areas as diverse as astronomy, geophysics, medicine, social media, and finance. In astronomy, for instance, we now have long and diverse series of star magnitude and spectra, radio-astronomy signals, asteroid position measurements, and other records. In medicine, we have very long and multiple records of physical activity indicators, heart rate, and other biological features. In social media and social studies, we have long records of human interactions, from administrative data to internet activities. In finance, we have tic-by-tic data of asset prices from many markets and firms. The diversity and length of data available to researchers leads to particular challenges when comparing and clustering time series. For these tasks it is not usually possible to use traditional methods of analysing, estimating models, and comparing features, as these methods imply computing and inverting extremely large matrices. We propose and will investigate a spectral method of synthesizing and comparing time series characteristics which is nonparametric and focused on the data cyclical features. Instead of using all the information available from data, which is computationally very expensive, this procedure we will use regularization rules in order to select and summarize the most relevant information for clustering purposes. This method does not imply the computation of the full periodograms, but only of the periodogram components around the frequencies of interest. It then proceeds to comparing the periodogram ordinates for the various time series and grouping them with common clustering methods. We call it a fragmented-periodogram approach. |