Feature Overview Table
A high-level summary table of all catch22 features.
Note: All catch22 features are statistical properties of the z-scored time series - they aim to focus on the properties of the time-ordering of the data and are insensitive to the raw values in the time series.
In the following table, we give the original feature name (from the Lubba et al. (2019) paper), and a shorter name more suitable for use in feature descriptions. Features are also (loosely) categorised into broader conceptual groupings.
Each feature is listed according to the order in which it, along with its associated value, is returned by catch22 e.g., the first feature returned (i.e., #1) is always DN_HistogramMode_5.
# | Feature name | Short name | Category | Description |
---|---|---|---|---|
1 |
|
| 5-bin histogram mode | |
2 |
|
| 10-bin histogram mode | |
3 |
|
| Positive outlier timing | |
4 |
|
| Negative outlier timing | |
5 |
|
| First crossing of the ACF | |
6 |
|
| First minimum of the ACF | |
7 |
|
| Power in lowest 20% frequencies | |
8 |
|
| Centroid frequency | |
9 |
|
| Error of 3-point rolling mean forecast | |
10 |
|
| Change in autocorrelation timescale after incremental differencing | |
11 |
|
| Proportion of high incremental changes in the series | |
12 |
|
| Longest stretch of above-mean values | |
13 |
|
| Longest stretch of decreasing values | |
14 |
|
| Entropy of successive pairs in symbolized series | |
15 |
|
| Histogram-based automutual information (lag 2, 5 bins) | |
16 |
|
| Time reversibility | |
17 |
|
| First minimum of the AMI function | |
18 |
|
| Transition matrix column variance | |
19 |
|
| Wang's periodicity metric | |
20 |
|
| Goodness of exponential fit to embedding distance distribution | |
21 |
|
| Rescaled range fluctuation analysis (low-scale scaling) | |
22 |
|
| Detrended fluctuation analysis (low-scale scaling) |
Catch24 (Catch22 + Mean + Std)
In some cases, in which scale and spread of the raw time-series values may be relevant to class differences, the two simple distributional moment features (using the catch24
flag in the software implementations) can be added. This will result in 24 features being calculated: the catch22 features in addition to the mean and standard deviation.
# | Feature name | Short name | Description |
---|---|---|---|
23 |
|
| Mean |
24 |
|
| Standard deviation |
Feature Dependencies
Although the selection framework used to generate the catch22 feature set included a step to reduce redundancy, it was not designed to generate an independent set of features.
Pairwise Spearman Correlations
Below is an example of the generic non-independence of features. We have plotted the Spearman correlation coefficient between all pairs of features, quantifying the similarity of their outputs across a diverse range of 1000 empirical time series:
We find a large cluster of features sensitive to the autocorrelation of a time series.
We also find a small cluster of two highly correlated features,
DN_HistogramMode_5
andDN_HistogramMode_10
, which measure the mode of the z-scired time-series distribution using different numbers of bins.
This dependency structure should be taken in mind when interpreting the result of catch22 analyses: Does your dataset exhibit any of these generic dependencies, or some unique dependencies?
PC Loadings
Below is a similar plot, but with colour overlayed according to the weights onto the first three principal components:
Broadly,
The first two principal components capture different aspects of the autocorrelation structure.
The third principal component captures different aspects of the distribution symmetry.
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