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Quick start

These snippets mirror the runnable example vignettes, which are exercised in CI so they stay in sync with the package.

Partial moments

import numpy as np
from nns import lpm, upm

x = np.array([-2.0, -1.0, 0.5, 3.0], dtype=np.float64)

lower = lpm(degree=2, target=0.0, x=x)
upper = upm(degree=2, target=0.0, x=x)

print("lower partial moment:", lower)
print("upper partial moment:", upper)

Nonlinear dependence

import numpy as np
from nns import nns_cor, nns_dep

grid = np.linspace(-2.0, 2.0, 80, dtype=np.float64)
y = grid**2

print("NNS dependence:", nns_dep(grid, y))
print("NNS correlation:", nns_cor(grid, y))

Nonlinear regression

Fit a nonlinear regression and estimate new points:

import numpy as np
from nns import nns_reg

x = np.linspace(-3.0, 3.0, 80, dtype=np.float64)
y = np.sin(x) + 0.2 * x
points = np.array([-1.5, 0.0, 1.5], dtype=np.float64)

fit = nns_reg(x, y, point_est=points, confidence_interval=None)

print("R2:", fit["R2"])
print(np.column_stack((points, fit["Point.est"])))

Forecasting

Forecast a univariate series:

import numpy as np
from nns import nns_arma, nns_seas

t = np.arange(1, 60, dtype=np.float64)
series = 10.0 + np.sin(t / 3.0) + 0.05 * t

seasonality = nns_seas(series, modulo=[3, 4, 6], mod_only=True)
forecast = nns_arma(series, h=3, seasonal_factor=4, method="lin")

print("best seasonal period:", seasonality["best.period"])
print("forecast:", forecast)

Next steps