def l1_fit(index, y, beta_d2=1.0, beta_d1=1.0, beta_seasonal=1.0,
beta_step=5.0, period=12, growth=0.0, step_permissives=None):
assert isinstance(y, np.ndarray)
assert isinstance(index, np.ndarray)
#x must be integer type for seasonality to make sense
assert index.dtype.kind == 'i'
n = len(y)
m = n-2
p = period
ys, y_min, y_max = mu.scale_numpy(y)
D1 = mu.get_first_derivative_matrix_nes(index)
D2 = mu.get_second_derivative_matrix_nes(index)
H = mu.get_step_function_matrix(n)
T = mu.get_T_matrix(p)
B = mu.get_B_matrix_nes(index, p)
Q = B*T
#define F_matrix from blocks like in paper
zero = mu.zero_spmatrix
ident = mu.identity_spmatrix
gvec = spmatrix(growth, range(m), [0]*m)
zero_m = spmatrix(0.0, range(m), [0]*m)
zero_p = spmatrix(0.0, range(p), [0]*p)
zero_n = spmatrix(0.0, range(n), [0]*n)
step_reg = mu.get_step_function_reg(n, beta_step, permissives=step_permissives)
F_matrix = sparse([
[ident(n), -beta_d1*D1, -beta_d2*D2, zero(p, n), zero(n)],
[Q, zero(m, p-1), zero(m, p-1), -beta_seasonal*T, zero(n, p-1)],
[H, zero(m, n), zero(m, n), zero(p, n), step_reg]
])
w_vector = sparse([
mu.np2spmatrix(ys), gvec, zero_m, zero_p, zero_n
])
solution_vector = np.asarray(l1.l1(matrix(F_matrix), matrix(w_vector))).squeeze()
#separate
xbase = solution_vector[0:n]
s = solution_vector[n:n+p-1]
h = solution_vector[n+p-1:]
#scale back to original
if y_max > y_min:
scaling = y_max - y_min
else:
scaling = 1.0
xbase = xbase*scaling + y_min
s = s*scaling
h = h*scaling
seas = np.asarray(Q*matrix(s)).squeeze()
steps = np.asarray(H*matrix(h)).squeeze()
x = xbase + seas + steps
solution = {'xbase': xbase, 'seas': seas, 'steps': steps, 'x': x, 'h': h, 's': s}
return solution
def l1_fit(
index, y, beta_d2=1.0, beta_d1=1.0, beta_seasonal=1.0, beta_step=1000.0, growth=0.0, seasonality_matrix=None
):
"""
Least Absolute Deviation Time Series fitting function
lower-level than version operating on actual dates
:param index: ndarray, index of numeric x-values representing time
:param y: ndarray, the time-series y-values
:param beta_d2: L1 regularization parameter on the second derivative
:param beta_d1: L1 regularization parameter on the first derivative
:param beta_seasonal: L1 regularization parameter on the
seasonal components
:param beta_step: L1 regularization parameter on the
step-function components
:param growth: the default growth rate that is regularized toward
default 0
:param seasonality_matrix:
matrix which maps seasonality variables onto the index of data points
allows the problem to be written in purely matrix form
comes from get_seasonality_matrix function
:return:
"""
# print "beta_d2: %s" % beta_d2
# print "beta_seasonal: %s" % beta_seasonal
assert isinstance(y, np.ndarray)
assert isinstance(index, np.ndarray)
# x must be integer type for seasonality to make sense
# assert index.dtype.kind == 'i'
# dimensions
n = len(y)
m = n - 2
p = seasonality_matrix.size[1]
ys, y_min, y_max = mu.scale_numpy(y)
# set up matrices
d1 = mu.get_first_derivative_matrix_nes(index)
d2 = mu.get_second_derivative_matrix_nes(index)
h = mu.get_step_function_matrix(n)
t = mu.get_T_matrix(p)
q = seasonality_matrix * t
zero = mu.zero_spmatrix
ident = mu.identity_spmatrix
gvec = spmatrix(growth, range(m), [0] * m)
zero_m = spmatrix(0.0, range(m), [0] * m)
zero_p = spmatrix(0.0, range(p), [0] * p)
zero_n = spmatrix(0.0, range(n), [0] * n)
# allow step-function regularization to change at some points
# is this really needed?
step_reg = mu.get_step_function_reg(n, beta_step)
# define F_matrix from blocks like in white paper
# so that the problem can be stated as a standard LAD problem
# and solvable with the l1 program
F_matrix = sparse(
[
[ident(n), -beta_d1 * d1, -beta_d2 * d2, zero(p, n), zero(n)],
[q, zero(m, p - 1), zero(m, p - 1), -beta_seasonal * t, zero(n, p - 1)],
[h, zero(m, n), zero(m, n), zero(p, n), step_reg],
]
)
# convert to sparse matrix
w_vector = sparse([mu.np2spmatrix(ys), gvec, zero_m, zero_p, zero_n])
# solve LAD problem and convert back to numpy array
solution_vector = np.asarray(l1.l1(matrix(F_matrix), matrix(w_vector))).squeeze()
# separate into components
base = solution_vector[0:n]
seasonal_parameters = solution_vector[n : n + p - 1]
step_jumps = solution_vector[n + p - 1 :]
# scale back to original
if y_max > y_min:
scaling = y_max - y_min
else:
scaling = 1.0
base = base * scaling + y_min
seasonal_parameters *= scaling
step_jumps *= scaling
seasonal_component = np.asarray(q * matrix(seasonal_parameters)).squeeze()
step_component = np.asarray(h * matrix(step_jumps)).squeeze()
model_without_seasonal = base + step_component
model = model_without_seasonal + seasonal_component
solution = {
"base": base,
"seasonal_component": seasonal_component,
"step_component": step_component,
"model": model,
"model_without_seasonal": model_without_seasonal,
"step_jumps": step_jumps,
"seasonal_parameters": seasonal_parameters,
}
return solution
