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我對過去4年每日基數的產品有需求。這種需求具有季節性季度的圖案，如以下的圖像基於時間序列的日常數據預測但季節性爲季度 - 在R
我願做這一數據基於時間序列預測。下面是我的代碼
myts = ts(forecastsku1$Value,frequency=90)
fit <- stl(myts, s.window="period")
plot(fit)
fit <- decompose(myts)
plot(fit)
這裏，而不是4個季節性因素TS創造90季節性因素，這不是我想要的。我想在3個月的時間內應用相同的季節性，然後進行預測。
數據僅供參考
dput(head(forecastsku1,100))
structure(list(date = structure(c(14625, 14626, 14627, 14628, 14629, 14630, 14631, 14632, 14633, 14634, 14635, 14636, 14637,
14638, 14639, 14640, 14641, 14642, 14643, 14644, 14645, 14646, 14647, 14648, 14649, 14650, 14651, 14652, 14653, 14654, 14655,
14656, 14657, 14658, 14659, 14660, 14661, 14662, 14663, 14664, 14665, 14666, 14667, 14668, 14669, 14670, 14671, 14672, 14673,
14674, 14675, 14676, 14677, 14678, 14679, 14680, 14681, 14682, 14683, 14684, 14685, 14686, 14687, 14688, 14689, 14690, 14691,
14692, 14693, 14694, 14695, 14696, 14697, 14698, 14699, 14700, 14701, 14702, 14703, 14704, 14705, 14706, 14707, 14708, 14709,
14710, 14711, 14712, 14713, 14714, 14715, 14716, 14717, 14718, 14719, 14720, 14721, 14722, 14723, 14724), class = "Date"),
Value = c(1407, 1413, 1407, 1406, 1401, 1410, 1411, 1416, 1404, 1409, 1414, 1414, 1400, 1421, 1398, 1404, 1397, 1404, 1407, 1409, 1406, 1395, 1397,
1403, 1412, 1399, 1409, 1393, 1405, 1403, 1406, 1402, 1405, 1386, 1393, 1405, 1397, 1393, 1402, 1402, 1393, 1391, 1410, 1402, 1408,
1394, 1404, 1398, 1406, 1389, 1401, 1391, 1394, 1384, 1377, 1390, 1395, 1399, 1384, 1397, 1398, 1384, 1377, 1394, 1398, 1394, 1391,
1403, 1382, 1390, 1385, 1403, 1390, 1388, 1391, 1384, 1392, 1390, 1381, 1387, 1395, 1390, 1388, 1384, 1387, 1395, 1380, 1378, 1383,
1384, 1232, 1247, 1232, 1248, 1236, 1236, 1231, 1237, 1224, 1236)),
.Names = c("date", "Value"), row.names = 13150:13249, class = "data.frame")
誰能幫助我在這種情況下？請讓我知道是否需要更多數據。
myts = ts(forecastsku1$Value,frequency=4)
fit <- decompose(myts)
plot(fit)
結果將是： 
請提供'dput（head（forecastsku1））'的輸出以使數據具有可重現性。 –
您可能需要先轉換數據。 –
@beginneR - 已添加數據... – vrajs5