# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 6 Concluding Remarks
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Series’** & \multicolumn{3}{c}{**h = 1**} & \multicolumn{3}{c}{**h = 3**} \\
**Code** & **NT** & **Std** & **Log** & **NT** & **Std** & **Log** \\ \hline A051 & 3.983 & 3.976 & 4.003 & 5.694 & 5.612 & 5.605 \\ A052 & 272.279 & 276.113 & 574.713 & 268.784 & 271.246 & 445.832 \\ A053 & 35.559 & 39.680 & 36.963 & 26.795 & 32.500 & 31.927 \\ A054 & 124.519 & 89.800 & 125.412 & 110.606 & 88.796 & 107.684 \\ A055 & 43.121 & 37.090 & 44.808 & 34.715 & 37.302 & 40.039 \\ A056 & 266.333 & 99.502 & 1.43E+12 & 287.931 & 214.556 & 9.42 \(\times\) 10\({}^{88}\) \\ A057 & 125.600 & 84.462 & 126.023 & 131.253 & 92.122 & 129.780 \\ A058 & 38.474 & 35.384 & 71.104 & 119.964 & 99.107 & 139.656 \\ A059 & 44.950 & 41.240 & 45.696 & 45.079 & 40.224 & 45.094 \\ A060 & 7.598 & 8.085 & 7.845 & 8.248 & 9.090 & 8.709 \\ A061 & 6819.116 & 7597.052 & 23,730.348 & 10,097.877 & 11,645.535 & 16,058.889 \\ A062 & 8.44 \(\times\) 10\({}^{6}\) & \(7.04\times\) 10\({}^{6}\) & \(1.37\times\) 10\({}^{7}\) & \(1.42\times\) 10\({}^{7}\) & \(8.94\times\) 10\({}^{6}\) & \(1.76\times\) 10\({}^{7}\) \\ A063 & 21.829 & 21.831 & 13.583 & 26.600 & 26.655 & 10.258 \\ A064 & 4393.038 & 3077.310 & 4376.077 & 5016.437 & 2925.211 & 4980.827 \\ A065 & 28.982 & 11.405 & 27.430 & 30.717 & 16.662 & 30.903 \\ A066 & 12.033 & 10.131 & 15.854 & 19.196 & 16.703 & 28.192 \\ A067 & 11,923.554 & 11,039.522 & 10,617.132 & 17,077.208 & 13,448.762 & 13,328.422 \\ A068 & 362.752 & 357.340 & 369.231 & 462.893 & 433.739 & 473.690 \\ A069 & 160.579 & 203.037 & 208.287 & 203.002 & 208.562 & 230.166 \\ A070 & 14.483 & 13.741 & 14.152 & 29.635 & 26.206 & 29.278 \\ A071 & 26.793 & 27.217 & 23.647 & 27.381 & 33.930 & 25.245 \\ A072 & 1379.200 & 1382.348 & 1472.325 & 1565.464 & 1624.687 & 1401.969 \\ A073 & 69.327 & 69.141 & 68.699 & 122.183 & 114.652 & 115.324 \\ A074 & 3294.883 & 2015.225 & 3445.829 & 3741.524 & 2288.009 & 3749.168 \\ A075 & 48.901 & 59.574 & 58.507 & 41.848 & 117.860 & 64.366 \\ A076 & 25,153.667 & 29,044.831 & 19,684.339 & 35,607.579 & 58,525.282 & 21,322.355 \\ A077 & 394.752 & 387.456 & 395.114 & 873.390 & 813.589 & 836.260 \\ A078 & 701.741 & 1275.259 & 790.650 & 1805.609 & 4921.674 & 1802.354 \\ A079 & 35.709 & 34.064 & 35.661 & 45.108 & 43.559 & 45.010 \\ A080 & 8.947 & 7.183 & 9.725 & 13.505 & 11.505 & 19.930 \\ A081 & 498.376 & 530.862 & 473.551 & 380.003 & 447.889 & 438.681 \\ A082 & 9.233 & 7.292 & 5.204 & 11.262 & 9.342 & 6.710 \\ A083 & 1.291 & 1.137 & 1.225 & 1.621 & 1.477 & 1.518 \\ A084 & 21,495.185 & 9111.162 & 11,832.143 & 32,355.027 & 9641.016 & 11,414.744 \\ A085 & 0.883 & 0.862 & 0.641 & 2.054 & 1.640 & 1.273 \\ A086 & 3.725 & 2.874 & 3.613 & 5.016 & 4.500 & 4.665 \\ A087 & 1.035 & 1.273 & 0.768 & 1.408 & 1.958 & 1.148 \\ A088 & 7.109 & 7.672 & 6.258 & 5.385 & 7.010 & 5.581 \\ A089 & 0.862 & 1.170 & 1.025 & 2.248 & 2.282 & 2.331 \\ A090 & 2.164 & 2.428 & 2.081 & 2.755 & 2.609 & 2.373 \\ A091 & 240.568 & 124.286 & 129.086 & 1376.708 & 148.271 & 160.964 \\ A092 & 3.35 \(\times\) 10\({}^{31}\) & 31,233.891 & 16,483.627 & 2.38\(\times\) 10\({}^{32}\) & 71,880.798 & 40,209.373 \\ A093 & 63.119 & 5.79\(\times\) 10\({}^{25}\) & 54.632 & 300.893 & 1.35\(\times\) 10\({}^{26}\) & 76.301 \\ A094 & 44,254.670 & 66,245.621 & 66,414.588 & 76,182.034 & 86,009.422 & 91,714.035 \\ A095 & 136.663 & 139.571 & 144.039 & 287.480 & 311.372 & 265.696 \\ A096 & 58.558 & 80.578 & 67.889 & 65.715 & 79.429 & 70.496 \\ A097 & 1.42\(\times\) 10\({}^{5}\) & 144,364.409 & 143,654.990 & 192,501.733 & 182,442.168 & 192,581.617 \\ A098 & 441.676 & 476.749 & 173.231 & 691.051 & 595.127 & 372.177 \\ A099 & 3.199 & 3.168 & 4.478 & 3.236 & 3.075 & 5.052 \\ A100 & 79.931 & 90.467 & 79.684 & 132.074 & 118.099 & 109.238 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Out-of-sample forecasting RMSFE (Continuation). \begin{table}