Feature-based time-series classification
Here, we consider a time series as a sequence of its segments approximated by parametric models (e.g. autoregressive model, discrete Fourier transform, discrete wavelet transform).
The parameters of the approximating models are used as time-series' features.
Then, we generalize this approach and use the distributions of the parameters estimated for models approximating different time-series' segments.
The proposed approach is applied to the problem of human activity recognition from accelerometer data.
M. E. Karasikov, V. V. Strijov, Feature-based time-series classification, Inform. Primen., 2016, Volume 10, Issue 4, 121–131.
pdf: full text
Matlab code resides in code.
All code rewritten in Python for reproducing experiments from the paper can be found in code/python.
Interactive human activity recognition application
The full code of the demonstration server can be found in code/activity_prediction/server.
Android client application
To get the full code of the android application, unpack ActivityPrediction.rar in code/activity_prediction/android.