Classification of Time-Series Data Based on Several Lag Windows

Abstract

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. (2006). Our metrics are applied to an economic time-series dataset.

Measuring Proximity Between Newspapers and Political Parties: The Sentiment Political Compass

“…In particular, the Boxcar (moving average) is often used for trend identification in time series problems in general, and for sentiment time series in particular (Giachanou & Crestani, ). We apply an extension of the moving average principle called the Bohman window (Kim & Park, ). The only difference to the moving average is that observations more distant in terms of the time dimension from the current time step, for which a smoothed sentiment value shall be computed, are weighted less compared with close observations.…”