Good morning, I hope someone can help with these questions, or perhaps suggest
one of the other R-lists?
I have two questions:
1. Why am I getting this warning?
2. Why is the second example "Point Forecast" the same value, I do
not see that in previous attempts with similar but different data sets as in
example 1?
Example1:
dat3 <- structure(c(3539122.86, 3081383.87, 4158672.31, 4137518.78,
4123682.08, 4819375.2, 4342687.77, 5028674.58, 4472145.07, 4967277.73,
4516240.31, 4876194.63, 4816446.59,
4887399.37, 5478504.85, 4871385.27, 5487543.68, 5464193.69,
5252591.03, 7071416.89, 5524350.89, 6107166.69, 6530003.55, 6445929.08,
7356743.81, 6750025.03,
6934714.08, 6656194.35
,-13913, -29385.31, -39633.37, -23487.13, -18202.86,
-57335.49, -26061.45, -60880.07, -17589.45, -35970.08, -89133.94,
-84694.58, -31724.89, -29847.95, -65421.74, -34334.22,
-48511.98, -30298.97, -38729.46, -29292.89, -46098.4, -65909.49,
-85879.23, -71845.28, -69017.07, -93161.03, -70847.29,
-85106.04
,-357694.19, -444792.75, -361349.57, -386717.55, -547422.05,
-518259.22, -417613.76, -578631.46, -804516.81, -572875.52, -510487.53,
-666294.87, -673233.37, -556564.45, -963346.75, -639288.2,
-910104.23, -773428.8, -1008078.84, -546685.3, -729932.94, -987098.23,
-964001.63, -986995.93, -680066.58, -728854.58, -730766.92,
-753861.59)
,.Dim = c(28L, 3L)
,.Dimnames = list(NULL,
c("OONNetRev","OONAdjusted" ,"OONCancelled"))
,.Tsp = c(2016, 2018.25, 12), class = c("mts",
"ts", "matrix"))
head(dat3); nrow(dat3)
TNR_moving_average <- forecast(ma(dat3[1:28], order=3), h=8)
TNR_moving_average
# Warning message:
# In ets(object, lambda = lambda, biasadj = biasadj,
allow.multiplicative.trend = allow.multiplicative.trend, :
# Missing values encountered. Using longest contiguous portion of
time series
# > Point Forecast Lo 80 Hi 80 Lo 95
Hi 95
# 28 7007065.99688 6675015.72012 7339116.27365 6499238.92148
7514893.07229
# 29 7135745.42473 6721543.41996 7549947.42950 6502278.12345
7769212.72601
# 30 7264424.85258 6779532.18065 7749317.52450 6522845.50541
8006004.19974
# 31 7393104.28042 6844496.10189 7941712.45896 6554080.47486
8232128.08599
# 32 7521783.70827 6914203.11991 8129364.29663 6592569.38486
8450998.03168
# 33 7650463.13612 6987355.72657 8313570.54567 6636327.86794
8664598.40429
# 34 7779142.56396 7063123.29787 8495161.83005 6684085.59434
8874199.53358
# 35 7907821.99181 7140937.69145 8674706.29217 6734973.66528
9080670.31834
Example2:
dat3 <- structure(c(994320.58, 811664.54, 1045259.43, 951659.48, 669458.94,
986741.09, 1023344.82, 938971.65, 897670.06, 1040074.6, 1090310.01,
1289821.17, 1187806.23, 971485.76, 1161147.42, 870585.04,
1021301.52, 1215798.03, 1015004.43, 1365863.09, 995769.41,
1331725.36, 1271032.91, 1092103.82, 1297131.4, 1129195.28,
1372594.58, 1553717.57,
-39811.51, -47356.74, -49046.86, -41311.13, -79063.98,
-43916.59, -16746.33, -38347.9, -84797.44, -38961.44,
-72036.83, -62854.78, -35259.84, -44198.39, -34262.65,
-49245.82, -34977.28, -36797.35, -47534.43, -33515.13,
-25764.41, -29130.53, -57693.63, -51026.83, -49624.49,
-36508.13, -32667.21, -37900.5,
-247443.87, -372942.34, -344080.78, -355586.21, -458998.84,
-378086.44, -333994.18, -567024.45, -521499.8, -428915.13,
-512034.28, -440865.42, -347494.22, -422436.19, -444588.65,
-462891.57, -518395.47, -373818.5, -398899.53, -381573.69,
-531449.2, -476238.48, -434296.86, -655679.94, -528999.52,
-423725.95, -556977.31, -518633.95)
,.Dim = c(28L, 3L)
,.Dimnames = list(NULL,
c("EditNetRev","EditNetAdjusted"
,"EditNetCancelled"))
,.Tsp = c(2016, 2018.25, 12), class = c("mts",
"ts", "matrix"))
head(dat3); nrow(dat3)
TNR_moving_average <- forecast(ma(dat3[1:28], order=3), h=8)
TNR_moving_average
# Warning message:
# In ets(object, lambda = lambda, biasadj = biasadj, allow.multiplicative.trend
= allow.multiplicative.trend, :
# Missing values encountered. Using longest contiguous portion of time
series
# > TNR_moving_average
# Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
# 28 1351827.24389 1246570.02118 1457084.46661 1190850.213255
1512804.27453
# 29 1351827.24389 1202841.21791 1500813.26987 1123972.779844
1579681.70794
# 30 1351827.24389 1169192.03201 1534462.45578 1072510.790913
1631143.69687
# 31 1351827.24389 1140745.29674 1562909.19105 1029005.263624
1674649.22416
# 32 1351827.24389 1115613.58783 1588040.89996 990569.631639
1713084.85615
# 33 1351827.24389 1092829.78856 1610824.69923 955724.817592
1747929.67020
# 34 1351827.24389 1071819.89899 1631834.58880 923592.964312
1780061.52348
# 35 1351827.24389 1052210.25675 1651444.23104 893602.604521
1810051.88327
Thank you for any advice or direction.
WHP
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