library(seasonal) x <- structure(c(620L, 585L, 643L, 706L, 660L, 721L, 678L, 653L, 638L, 789L, 1332L, 3226L, 598L, 576L, 683L, 735L, 685L, 722L, 745L, 682L, 671L, 799L, 1480L, 3275L, 618L, 637L, 819L, 782L, 742L, 758L, 778L, 801L, 806L, 987L, 1677L, 3445L, 683L, 665L, 823L, 867L, 821L, 873L, 851L, 846L, 846L, 993L, 1830L, 3616L, 705L, 737L, 890L, 862L, 669L, 849L, 877L, 874L, 870L, 1094L, 2002L, 4073L, 730L, 791L, 1032L, 896L, 947L, 940L, 963L, 958L, 971L, 1141L, 2009L, 3643L, 858L, 883L, 987L, 1022L, 986L, 1016L, 1042L, 1025L, 1005L, 1197L, 2011L, 3801L, 933L, 966L, 1135L, 1076L, 1061L, 1101L, 1083L, 1101L, 1111L, 1277L, 2096L, 3711L, 893L, 1002L, 1106L, 1136L, 1072L, 1098L, 1124L, 1132L, 1175L, 1285L, 2161L, 3763L, 880L, 971L, 1176L, 1063L, 1038L, 1107L, 1094L, 1065L, 1050L, 1303L, 2445L, 3628L, 896L, 966L, 1186L, 1014L, 1078L, 1065L, 1103L, 1087L, 1048L, 1347L, 2468L, 3651L, 910L, 940L, 1111L, 1139L, 1088L, 1064L, 1094L, 1087L, 1095L, 1286L, 2223L, 3545L, 962L, 1045L, 1125L, 1082L, 974L, 1024L, 1086L, 1016L, 1051L, 1311L, 2187L, 3451L, 950L, 980L, 1173L, 1098L, 1061L, 1049L, 1077L, 1028L, 1019L, 1229L, 2120L, 3471L, 1063L, 1022L, 1117L, 1094L, 1040L, 1048L, 1070L, 1043L, 1086L, 1205L, 1933L, 3299L, 948L, 974L, 1188L, 1064L, 1050L, 1096L, 1102L, 1084L, 1102L, 1275L, 2208L, 3253L, 1043L, 1077L, 1183L, 1080L, 1131L, 1044L, 1097L, 1065L, 1061L, 1218L, 1959L, 3117L, 1031L, 1009L, 1095L, 1086L, 1064L, 1020L, 1058L, 1007L, 1063L, 1194L, 1842L, 2999L, 953L, 1000L, 1155L, 1059L, 1030L, 1024L, 1070L, 1039L, 1074L, 1254L, 1990L, 3064L, 967L, 1023L, 1193L, 1184L, 1065L, 1061L, 1082L, 1067L, 1143L, 1243L, 1951L, 3033L, 1063L, 1182L, 1259L, 1146L, 1175L, 1120L, 1113L, 1117L, 1148L, 1263L, 1929L, 2963L, 1019L, 1068L, 1239L, 1107L, 1116L, 1057L, 1106L, 1145L, 1161L, 1338L, 2134L, 3263L, 1012L, 1084L, 1213L, 1195L, 1170L, 1084L, 1183L, 1185L, 1253L, 1360L, 2140L, 3511L, 1157L, 1190L, 1360L, 1260L, 1274L, 1196L), .Tsp = c(1992, 2015.4166666666, 12), class = "ts") m0 <- seas(x, x11 = "") x13page(m0, "series.span")
Let's assume there is to be a discussion about two possible prior adjustment methods.
take a log transform and let auto AO detection
no transform and include more AO's
mA <- seas(x, transform.function="log", x11.seasonalma = "s3x5", forecast.maxlead = "12", outlier.types = "AO", regression.aictest = "td") x13page(mA, "main")
We get two AO's:
mB <- seas(x, transform.function="none", x11.seasonalma = "s3x5", forecast.maxlead = "12", outlier.types = "AO", regression.aictest = "td") x13page(mB, "main")
We get 7 AO's now
mAE <- seas(x, transform.function="log", x11.seasonalma = "s3x5", forecast.maxlead = "12", outlier.types = "AO", regression.aictest = "td, easter") x13page(mAE, "main")
One idea would be to add the easter regressor in. For Hobbies, Toys and games this seems prevalent.
However, Neither does easter ectify any outliers for model A...
mBE <- seas(x, transform.function="none", x11.seasonalma = "s3x5", forecast.maxlead = "12", outlier.types = "AO", regression.aictest = "td, easter") x13page(mBE, "main")
... or for model B
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.