Using the online covariance estimators
- Choose a covariance “skater” from the listing of cov skaters
- Import it
- Pass it one data vector or list at a time
Example usage
from precise.skaters.covariance.ewapm import ewa_pm_emp_scov_r005_n100 as f
s = {}
for y in ys:
x, x_cov, s = f(s=s, y=y)
Interpreting covariance skater names
Examples:
| Skater name | Location | Meaning |
|---|---|---|
| buf_huber_pcov_d1_a1_b2_n50 | skaters/covariance/bufhuber | Applies an approach that exploits Huber pseudo-means to a buffer of data of length 50 in need of differencing once, with generalized Huber loss parameters a=1, b=2. |
| buf_sk_ld_pcov_d0_n100 | skaters/covariance/bufsk | Applies sk-learn’s implementation of Ledoit-Wolf to stationary buffered data of length 100 |
| ewa_pm_emp_scov_r01 | skaters/covariance/ewapartial | Performs an incremental, recency-weighted sample covariance estimate that exploits partial moments. Uses a memory parameter r=0.01 |
Broad calculation style categories
| Shorthand | Interpretation | Incremental ? |
|---|---|---|
| buf | Performs classical batch calculation on a fixed window of data each time | No |
| win | Performs incremental fixed window calculation. | Yes |
| run | Running calculation weighing all observations equally | Yes |
| ewa | Running calculation weighing recent observations more | Yes |
Methodology hints (can be combined)
| Shorthand | Inspiration |
|---|---|
| emp | “Empirical” (not shrunk or augmented) |
| lz | Le-Zhong variable-by-variable updating |
| lw | Ledoit-Wolf |
| pm | Partial moments |
| huber | Generalized Huber pseudo-mean |
| oas | Oracle approximating shrinkage. |
| gl | Graphical Lasso |
| mcd | Minimum covariance determinant |
| weak | Novel shrinkage method by yours truly |
Intended main target (more than one may be produced in the state)
| Shorthand | Intent |
|---|---|
| scov | Sample covariance |
| pcov | Population covariance |
| spre | Inverse of sample covariance |
| ppre | Inverse of population covariance |
Differencing hints:
| Shorthand | Intent |
|---|---|
| d0 | For use on stationary, ideally IID data |
| d1 | For use on data that is iid after taking one difference |
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