Find more info on our website.
A Weka compatible Java toolbox for time series classification, clustering and transformation. Eventually, we would like to support:
We are looking at getting this on Maven. For now there are two options:
- download the Jar File
- download the source file and include in a project in your favourite IDE
you can then construct your own experiment (see BasicExamples.java) or the experimental structure we use (see Experiments.java)
We have implemented the following bespoke classifiers for univariate, equal length time series classification
Distance Based
- DD_DTW
- DTD_C
- ElasticEnsemble
- NN_CID
- SAX_1NN
- SAXVSM
- ProximityForest
Dictionary Based
- BOSS
- BOP
- WEASEL
Spectral Based
- RISE
- CRISE
Shaplet Based
- LearnShapelets
- ShapeletTransformClassifier
- FastShapelets
(to do: recover original ShapeletTree)
Interval Based
- TSF
- TSBF
- LPS
Ensembles
- FlatCote
- HiveCote
We have implemented the following bespoke classifiers for multivariate, equal length time series classification
- NN_ED_D
- NN_ED_I
- NN_DTW_D
- NN_DTW_I
- NN_DTW_A
- MultivariateShapeletTransformClassifier
- ConcatenateClassifier
Currently quite limited. Standard approach would be to perform an unsupervised
- UnsupervisedShapelets
SimpleBatchFilters that take an Instances (the set of time series), transforms them and returns a new Instances object
- ACF
- ACF_PACF
- ARMA
- BagOfPatternsFilter
- BinaryTransform
- Clipping
- Correlation
- Cosine
- DerivativeFilter
- Differences
- FFT
- Hilbert
- MatrixProfile
- NormalizeAttribute
- NormalizeCase
- PAA
- PACF
- PowerCepstrum
- PowerSepstrum
- RankOrder
- RunLength
- SAX
- Sine
- SummaryStats