Adaptive Learning, Stock Market, Predictions, Machine Learning, Artificial Intelligence.
range(0,M): xn = np.block([[xd[n]], [xn[0:N]]]); y[n]= np.matmul(wn.T, xn); if(n>M-l-1): e =0; else: e=int(x[n]-y[n]); wn = wn + 2*u*e*xn; return y,wn; x = np.array([1655, 1648, 1615, 1638, 1685, 1729, 1754, 1770, 1780, 1785, 1800, 1800, 1754, 1718, 1716, 1795, 1787, 1797, 1751, 1811, 1845, 1864,