labels=["GP Estimation (Opt)", "Time", "Price"],
nf=1)
## Plot realizations !!
# f_s = gp.generate_process(x_grid, N = 20)
# gl.plot(x_grid,f_s, legend = ["Realization"], nf = 0)
timeSeriesflag = 1
if (timeSeriesflag):
# TODO: The peaks of variance are cause by the weekends ! They are further.
# How to model this in the algorithm !
sigma_eps = None # For not plotting shit
ws = 20
lag = 1
y_pred, Cov = gp.OneStepWindowedPrediction(dates,
timeSeries,
ws=ws,
lag=lag)
y_pred = np.array(y_pred)
Cov = np.array(Cov)
sigma = np.sqrt(Cov)
gl.plot_timeRegression(dates[ws + lag:y_pred.size + ws + lag, :],
y_pred,
sigma,
dates,
timeSeries,
sigma_eps,
labels=[
"GP OneStepPrediction lag = %i (Opt)" % lag,
"Time", "Price"
],
