The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
data("LakeHuron")
fit_LH <- fastTS(LakeHuron)
fit_LH
#> An endogenous PACF-based fastTS model.
#>
#> PF_gamma AICc_d BIC_d
#> 0.00 4.17 6.56
#> 0.25 3.34 3.54
#> 0.50 2.98 3.22
#> 1.00 1.06 1.24
#> 2.00 *0* *0*
#> 4.00 6.79 2.9
#> 8.00 6.79 2.9
#> 16.00 6.79 2.9
#>
#> AICc_d and BIC_d are the difference from the minimum; *0* is best.
#>
#> - Best AICc model: 4 active terms
#> - Best BIC model: 4 active terms
#>
#> Test-set prediction accuracy (20% held-out test set)
#> rmse rsq mae
#> AICc 0.7751 0.6043019 0.5888855
#> BIC 0.7751 0.6043019 0.5888855
coef(fit_LH)
#> 0.00069
#> (Intercept) 111.8740292
#> lag1 1.1003545
#> lag2 -0.4732437
#> lag3 0.1796316
#> lag4 0.0000000
#> lag5 0.0000000
#> lag6 0.0000000
#> lag7 0.0000000
#> lag8 0.0000000
#> lag9 0.0000000If you have a univariate time series with suspected trend, such as the EuStockMarkets data set,
data("EuStockMarkets")
X <- as.numeric(time(EuStockMarkets))
X_sp <- splines::bs(X-min(X), df = 9)
fit_stock <- fastTS(log(EuStockMarkets[,1]), n_lags_max = 400, X = X_sp, w_exo = "unpenalized")
fit_stock
#> A PACF-based fastTS model with 9 exogenous features.
#>
#> PF_gamma AICc_d BIC_d
#> 0.00 11.36 28.22
#> 0.25 2.18 4.41
#> 0.50 *0* 0.74
#> 1.00 6.46 <0.01
#> 2.00 6.46 *0*
#> 4.00 6.46 <0.01
#> 8.00 6.46 <0.01
#> 16.00 6.46 <0.01
#>
#> AICc_d and BIC_d are the difference from the minimum; *0* is best.
#>
#> - Best AICc model: 18 active terms
#> - Best BIC model: 10 active terms
#>
#> Test-set prediction accuracy (20% held-out test set)
#> rmse rsq mae
#> AICc 0.09071464 0.7328887 0.0758487
#> BIC 0.09292579 0.7197084 0.0780460
tail(coef(fit_stock), 11)
#> 0.0000096
#> lag399 0.000000000
#> lag400 0.000000000
#> 1 0.251818563
#> 2 -0.064008340
#> 3 -0.022147416
#> 4 -0.015421857
#> 5 -0.014734876
#> 6 0.001020223
#> 7 0.011708216
#> 8 0.311147538
#> 9 0.000000000
# insert plot? data("nottem")
fit_nt <- fastTS(nottem, n_lags_max = 24)
fit_nt
#> An endogenous PACF-based fastTS model.
#>
#> PF_gamma AICc_d BIC_d
#> 0.00 3.86 13.5
#> 0.25 0.43 *0*
#> 0.50 *0* 3.74
#> 1.00 2.49 3.91
#> 2.00 29.29 33.52
#> 4.00 83.31 75.53
#> 8.00 106.95 99.16
#> 16.00 212.75 201.97
#>
#> AICc_d and BIC_d are the difference from the minimum; *0* is best.
#>
#> - Best AICc model: 9 active terms
#> - Best BIC model: 6 active terms
#>
#> Test-set prediction accuracy (20% held-out test set)
#> rmse rsq mae
#> AICc 2.324769 0.9223185 1.747609
#> BIC 2.376213 0.9188426 1.815714
coef(fit_nt)
#> 0.0393
#> (Intercept) 11.89830169
#> lag1 0.40437830
#> lag2 0.00000000
#> lag3 -0.06838188
#> lag4 -0.09660138
#> lag5 -0.01640032
#> lag6 0.00000000
#> lag7 -0.04079986
#> lag8 0.00000000
#> lag9 0.00000000
#> lag10 0.00000000
#> lag11 0.16707390
#> lag12 0.00000000
#> lag13 0.05894906
#> lag14 0.00000000
#> lag15 0.00000000
#> lag16 0.00000000
#> lag17 0.00000000
#> lag18 0.00000000
#> lag19 0.00000000
#> lag20 0.00000000
#> lag21 0.00000000
#> lag22 0.00000000
#> lag23 0.00000000
#> lag24 0.34816025data("UKDriverDeaths")
fit_ukdd <- fastTS(UKDriverDeaths, n_lags_max = 24)
fit_ukdd
#> An endogenous PACF-based fastTS model.
#>
#> PF_gamma AICc_d BIC_d
#> 0.00 5.97 7.9
#> 0.25 3.91 3.91
#> 0.50 2.25 2.25
#> 1.00 0.77 0.77
#> 2.00 0.02 0.02
#> 4.00 *0* *0*
#> 8.00 10.21 7.55
#> 16.00 39.18 33.83
#>
#> AICc_d and BIC_d are the difference from the minimum; *0* is best.
#>
#> - Best AICc model: 5 active terms
#> - Best BIC model: 5 active terms
#>
#> Test-set prediction accuracy (20% held-out test set)
#> rmse rsq mae
#> AICc 170.9203 0.5776374 131.9969
#> BIC 170.9203 0.5776374 131.9969
coef(fit_ukdd)
#> 0.0282
#> (Intercept) 198.6573213
#> lag1 0.3805100
#> lag2 0.0000000
#> lag3 0.0000000
#> lag4 0.0000000
#> lag5 0.0000000
#> lag6 0.0000000
#> lag7 0.0000000
#> lag8 0.0000000
#> lag9 0.0000000
#> lag10 0.0000000
#> lag11 0.1645141
#> lag12 0.4682378
#> lag13 0.0000000
#> lag14 -0.1357345
#> lag15 0.0000000
#> lag16 0.0000000
#> lag17 0.0000000
#> lag18 0.0000000
#> lag19 0.0000000
#> lag20 0.0000000
#> lag21 0.0000000
#> lag22 0.0000000
#> lag23 0.0000000
#> lag24 0.0000000
data("sunspot.month")
fit_ssm <- fastTS(sunspot.month)
fit_ssm
#> An endogenous PACF-based fastTS model.
#>
#> PF_gamma AICc_d BIC_d
#> 0.00 24.92 38.93
#> 0.25 7.88 *0*
#> 0.50 *0* 0.48
#> 1.00 69.15 35.7
#> 2.00 221.33 131.01
#> 4.00 434.49 332.77
#> 8.00 434.49 332.77
#> 16.00 434.49 332.77
#>
#> AICc_d and BIC_d are the difference from the minimum; *0* is best.
#>
#> - Best AICc model: 23 active terms
#> - Best BIC model: 14 active terms
#>
#> Test-set prediction accuracy (20% held-out test set)
#> rmse rsq mae
#> AICc 15.94153 0.8920102 11.85384
#> BIC 16.04978 0.8905385 11.99382Model summaries
summary(fit_ssm)
#> Model summary (ncvreg) at optimal AICc (lambda=0.0514; gamma=0.5)
#> lasso-penalized linear regression with n=2224, p=317
#> At lambda=0.0514:
#> -------------------------------------------------
#> Nonzero coefficients : 22
#> Expected nonzero coefficients: 9.57
#> Average mfdr (22 features) : 0.435
#>
#> Estimate z mfdr Selected
#> lag1 0.543429 75.5671 < 1e-04 *
#> lag2 0.100936 14.3108 < 1e-04 *
#> lag9 0.095814 13.9892 < 1e-04 *
#> lag4 0.088981 12.7748 < 1e-04 *
#> lag3 0.077649 11.1365 < 1e-04 *
#> lag6 0.058591 8.7845 < 1e-04 *
#> lag5 0.031138 4.9444 0.00032487 *
#> lag18 -0.028505 -4.4224 0.00358883 *
#> lag102 0.021959 3.7402 0.05826384 *
#> lag27 -0.019987 -3.4042 0.10962497 *
#> lag212 -0.018947 -3.1479 0.18172134 *
#> lag24 -0.018334 -3.0999 0.22925539 *
#> lag111 0.017205 2.9704 0.42075053 *
#> lag92 0.017222 2.8639 0.56575685 *
#> lag12 0.012107 2.3759 1.00000000 *
#> lag20 -0.007329 -1.6906 1.00000000 *
#> lag21 -0.009982 -2.0002 1.00000000 *
#> lag34 -0.004472 -1.3302 1.00000000 *
#> lag55 -0.003429 -1.2274 1.00000000 *
#> lag96 0.001556 1.0625 1.00000000 *
#> lag275 0.000595 0.8962 1.00000000 *
#> lag292 -0.010658 -1.9653 1.00000000 *These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.