Source code for naginterfaces.​library.​examples.​tsa.​multi_inputmod_ex

#!/usr/bin/env python
"``naginterface.library.tsa.multi_inputmod`` Python Example."

# NAG Copyright 2017-2019.

# pylint: disable=invalid-name

from naginterfaces.library import tsa

[docs]def main(): """ Example for :func:`naginterfaces.library.tsa.multi_inputmod_estim` and :func:`naginterfaces.library.tsa.multi_inputmod_forecast_state`. Multivariate time series: estimation of multi-input model and forecasting. >>> main() naginterfaces.library.tsa.multi_inputmod Python Example Results. Multivariate time series: estimate and forecast. Begin estimation. Begin forecasting. The forecast values and their standard errors: No. Value Std error 1 97.306 4.788 2 101.785 6.328 3 103.812 7.145 4 102.073 7.360 Forecast components z(t): Series No.: 1: [175.413, 177.712, 179.373, 178.372] Output noise n(t): [-78.10695358 -75.92741605 -75.56034026 -76.29929963] """ print('naginterfaces.library.tsa.multi_inputmod Python Example Results.') print('Multivariate time series: estimate and forecast.') print('Begin estimation.') # Orders vector of the ARIMA model for the output noise component: mr = [1, 0, 0, 0, 0, 1, 4] # There is one input series. Here are the transfer function model orders: mt = [ [1, 0], [0, 0], [1, 0], [3, 0], ] # Multi-input model parameters: para = [0., 0., 2., 0.5, 0.] # Original undifferenced series: xxy = [ [8.075, 105.], [7.819, 119.], [7.366, 119.], [8.113, 109.], [7.380, 117.], [7.134, 135.], [7.222, 126.], [7.768, 112.], [7.386, 116.], [6.965, 122.], [6.478, 115.], [8.105, 115.], [8.060, 122.], [7.684, 138.], [7.580, 135.], [7.093, 125.], [6.129, 115.], [6.026, 108.], [6.679, 100.], [7.414, 96.], [7.112, 107.], [7.762, 115.], [7.645, 123.], [8.639, 122.], [7.667, 128.], [8.080, 136.], [6.678, 140.], [6.739, 122.], [5.569, 102.], [5.049, 103.], [5.642, 89.], [6.808, 77.], [6.636, 89.], [8.241, 94.], [7.968, 104.], [8.044, 108.], [7.791, 119.], [7.024, 126.], [6.102, 119.], [6.053, 103.], ] # Marginal likelihood: kef = 3 # Maximum required iterations: nit = 20 mim_estim = tsa.multi_inputmod_estim( mr, mt, para, xxy, kef=kef, nit=nit, ) print('Begin forecasting.') # Input series for the forecast: xxyn = [ [6.923, 0.], [6.939, 0.], [6.705, 0.], [6.914, 0.], ] # Orders for the input series: mrx = [ [2, 0], [0, 0], [2, 0], [0, 0], [1, 0], [1, 0], [4, 0], ] # Parameters for the input series: parx = [ [1.6743, 0.], [-0.9505, 0.], [1.4605, 0.], [-0.4862, 0.], [0.8993, 0.], ] # Estimated residual variances: rmsxy = [0.1720, 22.9256] mim_f = tsa.multi_inputmod_forecast_state( mim_estim.sttf, mr, mt, mim_estim.para, xxyn, mrx, parx, rmsxy, kzef=1, ) print('The forecast values and their standard errors:') print('No. Value Std error') for i, v in enumerate(mim_f.fva): print('{:3d} {:7.3f} {:7.3f}'.format(i+1, v, mim_f.fsd[i])) print('Forecast components z(t):') for i in range(mim_f.xxyn.shape[1] - 1): print('Series No.: {:d}:'.format(i + 1)) print( '[' + ', '.join( ['{:7.3f}']*mim_f.xxyn.shape[0] ).format(*mim_f.xxyn[:, i]) + ']' ) print('Output noise n(t):') print(mim_f.xxyn[:, -1])
if __name__ == '__main__': import doctest import sys sys.exit( doctest.testmod( None, verbose=True, report=False, optionflags=doctest.REPORT_NDIFF, ).failed )