Partial example of NLMS filter on sample stock data

nlms_stock_data.py

import pandas as pd
import numpy as np
import padasip as pa

SEED = 121
np.random.seed(SEED)

n = 5
s = ts['Close'].values.flatten()[: 1000]  # initial timeseries data
x = pa.input_from_history(s, n)  # input matrix
N = len(x)
d = np.zeros(len(x))

# define target
for i, k in enumerate(range((n-1), N)):
    d[i] = s[k+1]
    
# build filter on each mu
error_list = []
pa_list = []
for i, mu_ in enumerate([0.005, 0.05, 0.5]):
    f_nlms = pa.filters.FilterNLMS(n=n, mu=mu_, w='random')
    y, e, w = f_nlms.run(d, x)
    plot_filter_result(pred=y, actual=d, error=e, mu_val=mu_, n=n)
    _avg_error = round(np.mean(10*np.log10(e[: (len(e) - n)]**2)), 2)
    pa_list.append(f_nlms)
    error_list.append(_avg_error)