waverec

hana_ml.algorithms.pal.tsa.wavelet.waverec(dwt, wavelet=None, boundary=None)

Python wrapper for PAL multi-level inverse discrete wavelet transform.

Parameters:

dwtDWT

A DWT object containing wavelet coefficients as well as other related information for applying the inverse transformation.

waveletstr, optional

Specifies the wavelet filter used for discrete wavelet transform. Valid options include:

Daubechies family : 'db1' ~ 'db20'

Biorthogonal family: 'bior1.1', 'bior1.3', 'bior1.5', 'bior2.2', 'bior2.4', 'bior2.6', 'bior2.8', 'bior3.1', 'bior3.3', 'bior3.5', 'bior3.7', 'bior3.9', 'bior4.4', 'bior5.5', 'bior6.8'

Reverse Biorthogonal family : 'rbio1.1', 'rbio1.3', 'rbio1.5', 'rbio2.2', 'rbio2.4', 'rbio2.6', 'rbio2.8', 'rbio3.1', 'rbio3.3', 'rbio3.5', 'rbio3.7', 'rbio3.9', 'rbio4.4', 'rbio5.5', 'rbio6.8'

Coifman family: 'coif1' ~ 'coif5'

Symmetric family: 'sym2' ~ 'sym20'

If not provided, the value of dwt.wavelet will be used.

boundarystr, optional

Specifies the padding method for boundary values. Valid options include:

'zero' : Zero padding

'symmetric' : Symmetric padding

'periodic' : Periodic padding

'reflect' : Reflect padding

'smooth' : Smooth padding

'constant' : Constant padding

If not provided, the value of dwt.boundary will be used.

Returns:

DataFrame

The reconstructed time-series data from inverse discrete wavelet transform, structured as follows:

1st column : ID, type INTEGER, which reflects the order of time-series.

2nd column : VAL, type DOUBLE, the reconstructed time-series data(signal) from wavelet decomposition coefficients.

Examples

Assume dwt is a DWT object structured as follows:

>>> dwt.coeff_.collect()
    LEVEL  ID  COEFFICIENTS
0       0   0    451.442328
1       0   1    405.506091
...
16      2   3     78.767514
17      2   4    -70.401833

The original time-series data then can be reconstructed as follows:

>>> rec = waverec(dwt=dwt)
>>> rec.collect()
    ID  VALUE
0    0  266.0
1    1  145.9
...
12  12  194.3
13  13  149.5