我试图计算一些时间序列特征，当在R中使用tsfeatures函数时，我得到以下错误：

Warning messages:
1: In .f(.x[[i]], ...) : Insufficient data to compute STL decomposition
2: In .f(.x[[i]], ...) : Insufficient data to compute STL decomposition

对于每个时间序列，有365天的测量值，所以我对数据不足的警告感到困惑。
我怀疑这个错误可能与原始数据有关，我试图将面板数据强制转换为矩阵格式的时间序列，如下所示，但我可能搞砸了：

library(tsfeatures)

set.seed(25)

inds <- seq(as.Date("2022-05-01"), as.Date("2023-04-30"), by = "day")

values <- c(rnorm(length(inds), mean = 20, sd = 2),rnorm(length(inds), mean = 200, sd = 20))

products <- rep(c("A","B"), each = 365)

df <- data.frame(c(inds,inds),products,values)

df_mts <- ts(matrix(df$values, ncol = n_distinct(df$products), nrow = 365), frequency = 365, start = c(2022, as.numeric(format(as.Date("2022-05-01"), "%j"))))

features <- bind_cols(
  tsfeatures(df_mts, c("acf_features","entropy","lumpiness","flat_spots","crossing_points")),
  tsfeatures(df_mts,"stl_features", s.window = "periodic", robust = TRUE)
)

这是将每日数据转换为ts格式的正确方法吗？