def test_est_params():
rand_state = np.random.RandomState()
rand_state.seed(42)
# Generate a target series
AR = (np.array([1, 0.3, 0.5]), np.array([3, 1, 1]))
MA = (np.array([1, .1]), np.array([2, 1, 1]))
arma = ARMA(A=AR, B=MA, C=None, rand_state=rand_state)
series = arma.simulate(sampleT=1000)
# Estimate parameters by simulated series
AR_est = (np.array([1, .01, .01]), np.array([3, 1, 1]))
MA_est = (np.array([1, .01]), np.array([2, 1, 1]))
arma_est = ARMA(A=AR_est, B=MA_est, C=None)
arma_est.fix_constants()
arma_est.est_params(y=series)
nptest.assert_almost_equal(arma_est.A, arma.A, decimal=1)
nptest.assert_almost_equal(arma_est.B, arma.B, decimal=1)
def test_est_params():
rand_state = np.random.RandomState()
rand_state.seed(42)
# Generate a target series
AR = (np.array([1, 0.3, 0.5]), np.array([3, 1, 1]))
MA = (np.array([1, .1]), np.array([2, 1, 1]))
arma = ARMA(A=AR, B=MA, C=None, rand_state=rand_state)
series = arma.simulate(sampleT=1000)
# Estimate parameters by simulated series
AR_est = (np.array([1, .01, .01]), np.array([3, 1, 1]))
MA_est = (np.array([1, .01]), np.array([2, 1, 1]))
arma_est = ARMA(A=AR_est, B=MA_est, C=None)
arma_est.fix_constants()
arma_est.est_params(y=series)
nptest.assert_almost_equal(arma_est.A, arma.A, decimal=1)
nptest.assert_almost_equal(arma_est.B, arma.B, decimal=1)
def test_simulate_arma():
AR = (np.array([1, .5, .3, 0, .2, .1, 0, .2, .05, 1, .5, .3]),
np.array([3, 2, 2]))
MA = (np.array([1, .2, 0, .1, 0, 0, 1, .3]), np.array([2, 2, 2]))
arma = ARMA(A=AR, B=MA, C=None)
# noise generated with R for comparison
# ( setRNG(seed=0); noise <- makeTSnoise(10, 2, 2) )
w0_series0 = np.array([1.2629543, -0.3262334])
w0_series1 = np.array([-1.1476570, -0.2894616])
w_series0 = np.array(
[1.329799263, 1.272429321, 0.414641434, -1.539950042, -0.928567035,
-0.294720447, -0.005767173, 2.404653389, 0.763593461, -0.799009249])
w_series1 = np.array(
[-0.2992151, -0.4115108, 0.2522234, -0.8919211, 0.4356833, -1.2375384,
-0.2242679, 0.3773956, 0.1333364, 0.8041895])
noise = (np.vstack([w0_series0, w0_series1]).T,
np.vstack([w_series0, w_series1]).T)
# R simulation results ( arma <- ARMA(A=AR, B=MA, C=NULL);
# R_result <- simulate(arma, noise=noise, sampleT=10) )
series0 = np.array(
[1.58239012, 0.85063747, -0.11981462, -1.69017627, -0.19912156,
0.02830831, 0.16284912, 2.42364792, -0.15007052, -1.27531927])
series1 = np.array(
[-0.5172168, -0.4261651, 0.2958942, -0.8559883, 0.7033546, -1.0857290,
-0.2788928, 0.7393030, -0.2999778, 0.6363970])
R_result = np.vstack([series0, series1]).T
result = arma.simulate(sampleT=10, noise=noise)
nptest.assert_almost_equal(result, R_result)
# R simulations results with trend ( TREND <- c(1,2);
# arma_trend <- ARMA(A=AR, B=MA, C=NULL, TREND=TREND);
# R_result_trend <- simulate(arma_trend, noise=noise, sampleT=sampleT) )
TREND = np.array([1., 2.])
arma = ARMA(A=AR, B=MA, C=None, TREND=TREND)
series0 = np.array(
[2.5823901, 0.9506375, 0.2701854, -1.1311763, 0.1139784, 0.4486183,
0.6048481, 2.8091458, 0.2663152, -0.8590244])
series1 = np.array(
[1.48278321, 0.37383493, 1.17589420, 0.37601165, 1.67255459,
-0.05844899, 0.80133515, 1.76057423, 0.74401876, 1.68619050])
R_result = np.vstack([series0, series1]).T
result = arma.simulate(sampleT=10, noise=noise)
nptest.assert_almost_equal(result, R_result)
# R simulations results with trend and external series
# arma_trend_ext <- ARMA(A=AR, B=MA, C=X, TREND=TREND)
# input0 <- array(c(0.1, 0.2, 0.15, 0.05), dim=c(2,2))
# input <- array(c(0.1*c(1:10), 0.05*c(1:10)), dim=c(10,2))
# R_result_trend_ext <- simulate(arma_trend_ext, noise=noise,
# sampleT=sampleT, input0=input0, input=input)
X = (np.array([1, .3, 0, .05, 0, 0.1, 1, .3]), np.array([2, 2, 2]))
arma = ARMA(A=AR, B=MA, C=X, TREND=TREND)
input0 = np.array([0.1, 0.2, 0.15, 0.05]).reshape((2, 2), order='F')
input = np.hstack([0.1*np.arange(1, 11), 0.05*np.arange(1, 11)])
input = input.reshape((10, 2), order='F')
series0 = np.array(
[2.7273901, 1.0931375, 0.5122354, -0.8113013, 0.4892429, 0.9042891,
1.1312726, 3.4036172, 0.9345114, -0.1201090])
series1 = np.array(
[1.5827832, 0.4148349, 1.2723942, 0.5128017, 1.8170296, 0.1212361,
1.0094895, 1.9917395, 1.0044531, 1.9735374])
R_result = np.vstack([series0, series1]).T
result = arma.simulate(sampleT=10, noise=noise, u0=input0, u=input)
nptest.assert_almost_equal(result, R_result)
def test_simulate_arma():
AR = (np.array([1, .5, .3, 0, .2, .1, 0, .2, .05, 1, .5,
.3]), np.array([3, 2, 2]))
MA = (np.array([1, .2, 0, .1, 0, 0, 1, .3]), np.array([2, 2, 2]))
arma = ARMA(A=AR, B=MA, C=None)
# noise generated with R for comparison
# ( setRNG(seed=0); noise <- makeTSnoise(10, 2, 2) )
w0_series0 = np.array([1.2629543, -0.3262334])
w0_series1 = np.array([-1.1476570, -0.2894616])
w_series0 = np.array([
1.329799263, 1.272429321, 0.414641434, -1.539950042, -0.928567035,
-0.294720447, -0.005767173, 2.404653389, 0.763593461, -0.799009249
])
w_series1 = np.array([
-0.2992151, -0.4115108, 0.2522234, -0.8919211, 0.4356833, -1.2375384,
-0.2242679, 0.3773956, 0.1333364, 0.8041895
])
noise = (np.vstack([w0_series0,
w0_series1]).T, np.vstack([w_series0, w_series1]).T)
# R simulation results ( arma <- ARMA(A=AR, B=MA, C=NULL);
# R_result <- simulate(arma, noise=noise, sampleT=10) )
series0 = np.array([
1.58239012, 0.85063747, -0.11981462, -1.69017627, -0.19912156,
0.02830831, 0.16284912, 2.42364792, -0.15007052, -1.27531927
])
series1 = np.array([
-0.5172168, -0.4261651, 0.2958942, -0.8559883, 0.7033546, -1.0857290,
-0.2788928, 0.7393030, -0.2999778, 0.6363970
])
R_result = np.vstack([series0, series1]).T
result = arma.simulate(sampleT=10, noise=noise)
nptest.assert_almost_equal(result, R_result)
# R simulations results with trend ( TREND <- c(1,2);
# arma_trend <- ARMA(A=AR, B=MA, C=NULL, TREND=TREND);
# R_result_trend <- simulate(arma_trend, noise=noise, sampleT=sampleT) )
TREND = np.array([1., 2.])
arma = ARMA(A=AR, B=MA, C=None, TREND=TREND)
series0 = np.array([
2.5823901, 0.9506375, 0.2701854, -1.1311763, 0.1139784, 0.4486183,
0.6048481, 2.8091458, 0.2663152, -0.8590244
])
series1 = np.array([
1.48278321, 0.37383493, 1.17589420, 0.37601165, 1.67255459,
-0.05844899, 0.80133515, 1.76057423, 0.74401876, 1.68619050
])
R_result = np.vstack([series0, series1]).T
result = arma.simulate(sampleT=10, noise=noise)
nptest.assert_almost_equal(result, R_result)
# R simulations results with trend and external series
# arma_trend_ext <- ARMA(A=AR, B=MA, C=X, TREND=TREND)
# input0 <- array(c(0.1, 0.2, 0.15, 0.05), dim=c(2,2))
# input <- array(c(0.1*c(1:10), 0.05*c(1:10)), dim=c(10,2))
# R_result_trend_ext <- simulate(arma_trend_ext, noise=noise,
# sampleT=sampleT, input0=input0, input=input)
X = (np.array([1, .3, 0, .05, 0, 0.1, 1, .3]), np.array([2, 2, 2]))
arma = ARMA(A=AR, B=MA, C=X, TREND=TREND)
input0 = np.array([0.1, 0.2, 0.15, 0.05]).reshape((2, 2), order='F')
input = np.hstack([0.1 * np.arange(1, 11), 0.05 * np.arange(1, 11)])
input = input.reshape((10, 2), order='F')
series0 = np.array([
2.7273901, 1.0931375, 0.5122354, -0.8113013, 0.4892429, 0.9042891,
1.1312726, 3.4036172, 0.9345114, -0.1201090
])
series1 = np.array([
1.5827832, 0.4148349, 1.2723942, 0.5128017, 1.8170296, 0.1212361,
1.0094895, 1.9917395, 1.0044531, 1.9735374
])
R_result = np.vstack([series0, series1]).T
result = arma.simulate(sampleT=10, noise=noise, u0=input0, u=input)
nptest.assert_almost_equal(result, R_result)