q1 = np.asmatrix([
[-0., 0.],
[0., -0.],
])
p = [p1]#, p2] #, p3]
q = [q1]#q1]
# y0 = np.asmatrix([[0., 0., 0.]]).T #, [0., 0., 0.]
X = sim.varmapqGaussian(t = t, pMatrix = p, qMatrix = q)#, y0 = y0)
y = VARMAX(X.T, order = (1,1)).fit()
print(y.summary())
x1 = np.asarray(X[0,:]).reshape(t)
x2 = np.asarray(X[1,:]).reshape(t)
# x3 = np.asarray(X[2,:]).reshape(t)
# nprocess = X.shape[0]
pLag = len(p)
qLag = len(q)
#
params = logL.maxVARMApqN(X, pLag, qLag)
print(params)
df_transformed = df.diff().diff()
df_transformed = df_transformed.dropna()
# fourth, split data set
# ----------
nobs = 12 #number of observations for the test set
train = df_transformed.iloc[:
-nobs] #start = beginning of dataframe, or 0, to -12 from the end
test = df_transformed.iloc[-nobs:] #start at -12 from the end to go to the end
#fifth, fit the VARMA model
# ----------
# VARMAX needs as input the suggested p and q (no d) and a trend c which is the standard = linear trend
# fit requires both maxiter and disp, where disp is display=False => display less information
model = VARMAX(train, order=(1, 2), trend='c').fit(maxiter=500, disp=False)
print(model.summary())
#sixth, run a prediction vs test set
# ----------
df_forecast = model.forecast(nobs)
# sixth-bis, reverse the differencing to make values turn normal and understandable
# ----------
# we transform the differences by taking the cumulative sum of a column and adding it to a one line difference to revert it
df_forecast['Money_diff1x'] = (
df['Money'].iloc[-nobs - 1] -
df['Money'].iloc[-nobs - 2]) + df_forecast['Money_diff2x'].cumsum()
df_forecast['Money_Fcst'] = df['Money'].iloc[
-nobs - 1] + df_forecast['Money_diff1x'].cumsum()
df_forecast['Spending_diff1x'] = (
