The wavedec function in the hana_ml.algorithms.pal.tsa.wavelet module is a Python wrapper for the PAL multi-level discrete wavelet transform, which applies a discrete wavelet transform to time-series data using specified parameters such as wavelet filter, padding method for boundary values, decompose level, thresholding method, and thresholding values.
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Here is a Python code template based on the provided documentation:

```python
from hana_ml.algorithms.pal.tsa.wavelet import wavedec

# Assuming `data` is your DataFrame with time-series data
wavelet = 'db2'  # Choose the wavelet filter
key = None  # Optional: specify the time-stamp column
col = None  # Optional: specify the signal values column
boundary = 'symmetric'  # Optional: specify the padding method
level = 2  # Optional: specify the decompose level
compression = False  # Optional: specify whether to discard zero values
method = 'no'  # Optional: specify the thresholding method
threshold = 1e-9  # Optional: specify the uniform thresholding value
level_thresholds = None  # Optional: specify level-wise thresholding values

# Perform the wavelet transformation
dwt = wavedec(data=data, wavelet=wavelet, key=key, col=col, boundary=boundary, level=level, 
              compression=compression, method=method, threshold=threshold, level_thresholds=level_thresholds)

# Access the wavelet transformation result
coefficients = dwt.coeff_

# Print the result
print(coefficients.collect())
```

Please replace `'db2'` with the wavelet filter you want to use, and adjust the optional parameters as needed. The `data` variable should be a DataFrame containing your time-series data.