基于时间序列的每日数据预测，但季节性是季度 - 在R

Question

I have demand for a product on daily bases for last 4 years. This demand has quarterly seasonal patterns, as shown in following image

I would like to do time series based forecasting on this data. Following is my code

myts = ts(forecastsku1$Value,frequency=90)
fit <- stl(myts, s.window="period")
plot(fit)
fit <- decompose(myts)
plot(fit)

Here instead of 4 seasonal factor ts is creating 90 seasonal factor, which is not what I want. I want to apply same seasonality on 3 month duration and then do forecasting.

Data for reference

dput(head(forecastsku1,100))
structure(list(date = structure(c(14625, 14626, 14627, 14628, 14629, 14630, 14631, 14632, 14633, 14634, 14635, 14636, 14637, 
14638, 14639, 14640, 14641, 14642, 14643, 14644, 14645, 14646, 14647, 14648, 14649, 14650, 14651, 14652, 14653, 14654, 14655, 
14656, 14657, 14658, 14659, 14660, 14661, 14662, 14663, 14664, 14665, 14666, 14667, 14668, 14669, 14670, 14671, 14672, 14673, 
14674, 14675, 14676, 14677, 14678, 14679, 14680, 14681, 14682, 14683, 14684, 14685, 14686, 14687, 14688, 14689, 14690, 14691, 
14692, 14693, 14694, 14695, 14696, 14697, 14698, 14699, 14700, 14701, 14702, 14703, 14704, 14705, 14706, 14707, 14708, 14709, 
14710, 14711, 14712, 14713, 14714, 14715, 14716, 14717, 14718, 14719, 14720, 14721, 14722, 14723, 14724), class = "Date"), 
Value = c(1407, 1413, 1407, 1406, 1401, 1410, 1411, 1416, 1404, 1409, 1414, 1414, 1400, 1421, 1398, 1404, 1397, 1404, 1407, 1409, 1406, 1395, 1397, 
1403, 1412, 1399, 1409, 1393, 1405, 1403, 1406, 1402, 1405, 1386, 1393, 1405, 1397, 1393, 1402, 1402, 1393, 1391, 1410, 1402, 1408, 
1394, 1404, 1398, 1406, 1389, 1401, 1391, 1394, 1384, 1377, 1390, 1395, 1399, 1384, 1397, 1398, 1384, 1377, 1394, 1398, 1394, 1391, 
1403, 1382, 1390, 1385, 1403, 1390, 1388, 1391, 1384, 1392, 1390, 1381, 1387, 1395, 1390, 1388, 1384, 1387, 1395, 1380, 1378, 1383, 
1384, 1232, 1247, 1232, 1248, 1236, 1236, 1231, 1237, 1224, 1236)), 
.Names = c("date", "Value"), row.names = 13150:13249, class = "data.frame")

Can anyone help me in this case? Please let me know if more data required.

myts = ts(forecastsku1$Value,frequency=4)
fit <- decompose(myts)
plot(fit)

Result would be:

Answer 1

It is creating a 90 seasonal factor because your frequency is 90 in the ts definition. What you need to do is to specify a start and end in the ts and the period=4 so that the observations can be segregated the way you want them to be.. if you can successfully create a 4 seasonal factor, you can obviousy predict quarterly (4*3=12) . So instead of these dates I think it is more clear to have like start=c(2005,1) .Hopefully this is useful

Answer 2

this is an old question, but still, maybe my answer is of some value. You can seasonally adjust daily data using the dsa package (disclaimer: I'm the author).

I tried to replicate your time series (or something similar) to give you an idea of how to seasonally adjust them (the setting of the seasonal adjustment try to help modelling the jumping behaviour of the time series appropriately):

# loading packages
library(dsa); library(xts)

# Replication of the data
set.seed(23)
data <- seq(1250, 1000, , length.out=365.25*4) + rnorm(365.25*4, 0, 5)
time <- seq(as.Date("2008-01-01"), by="days", length.out=365.25*4)    
x <- xts(data, time)
ind <- as.numeric(format(zoo::index(x), "%m")) # Indicator of day of year
x[ind==1 | ind==2 | ind==3 | ind==7 | ind==8 | ind==9] <- 
x[ind==1 | ind==2 | ind==3 | ind==7 | ind==8 | ind==9] + 200

# Seasonally adjusting the data
result <- dsa(x, fourier_number=40, reiterate3=4, reg.create=NULL, cval=30) 
sa <- result$output[,1]
xtsplot(result$output[,c(2,1)], names=c("original", "seasonally adjusted"))
output(result) # creates a html in your working directory.