englianhu
commented
3 years ago https://github.com/JinmiaoChenLab/cytofkit/issues/17#issuecomment-416047706 and https://github.com/JinmiaoChenLab/cytofkit/issues/17#issuecomment-608450415
> mts <- smp %>%
+ msts(seasonal.periods = c(1440, nrow(smp)))
>
> mts <- llply(1:ncol(mts), function(i) {
+ y <- mts[,i] %>%
+ tbats %>%
+ forecast(h = 1440) %>%
+ as_tibble
+ names(y)[1] <- names(smp)[i]
+ y
+ }) %>%
+ bind_rows %>%
+ mutate(Model = factor('tbats'), Period = factor('dy.qt'), type = case_when(
+ !is.na(open) ~ 'open',
+ !is.na(high) ~ 'high',
+ !is.na(low) ~ 'low',
+ !is.na(close) ~ 'close')) %>%
+ dlply(.(type, Period), function(x) {
+ x %<>% dplyr::rename(open.Point.Forecast = open,
+ high.Point.Forecast = high,
+ low.Point.Forecast = low,
+ close.Point.Forecast = close)
+ names(x)[str_detect(names(x), '80|95')] <- paste0(x$type[1], '.', names(x)[str_detect(names(x), '80|95')])
+ x[colSums(!is.na(x)) > 0] %>%
+ data.frame %>%
+ as_tibble %>%
+ dplyr::select(-type)
+
+ }) %>%
+ join_all %>%
+ as_tibble
Joining by: Model, Period
Joining by: Model, Period
- Error: cannot allocate vector of size 11.1 Gb
> memory.size()
[1] 11984.01
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3767976 201.3 7315067 390.7 7315067 390.7
Vcells 12814470 97.8 1474157596 11247.0 1534873241 11710.2
> memory.limit()
[1] 16274memory.size() occupied 75% of memory.limit()
> mts <- smp %>%
+ msts(seasonal.periods = c(1440, nrow(smp)))
>
> mts <- llply(1:ncol(mts), function(i) {
+ y <- mts[,i] %>%
+ tbats %>%
+ forecast(h = 1440) %>%
+ as_tibble
+ names(y)[1] <- names(smp)[i]
+ y
+ }) %>%
+ bind_rows %>%
+ mutate(Model = factor('tbats'), Period = factor('dy.qt'), type = case_when(
+ !is.na(open) ~ 'open',
+ !is.na(high) ~ 'high',
+ !is.na(low) ~ 'low',
+ !is.na(close) ~ 'close')) %>%
+ dlply(.(type, Period), function(x) {
+ x %<>% dplyr::rename(open.Point.Forecast = open,
+ high.Point.Forecast = high,
+ low.Point.Forecast = low,
+ close.Point.Forecast = close)
+ names(x)[str_detect(names(x), '80|95')] <- paste0(x$type[1], '.', names(x)[str_detect(names(x), '80|95')])
+ x[colSums(!is.na(x)) > 0] %>%
+ data.frame %>%
+ as_tibble %>%
+ dplyr::select(-type)
+
+ })
> mtss <- mts %>% bind_cols
New names:
* Model -> Model...6
* Period -> Period...7
* Model -> Model...13
* Period -> Period...14
* Model -> Model...20
* ...
> mtss
# A tibble: 1,440 x 28
close.Lo.80 close.Hi.80 close.Lo.95 close.Hi.95 close.Point.For~ Model...6
<dbl> <dbl> <dbl> <dbl> <dbl> <fct>
1 118. 118. 118. 118. 118. tbats
2 118. 118. 118. 118. 118. tbats
3 118. 118. 118. 118. 118. tbats
4 118. 118. 118. 118. 118. tbats
5 118. 118. 118. 118. 118. tbats
6 118. 118. 118. 118. 118. tbats
7 118. 118. 118. 118. 118. tbats
8 118. 118. 118. 118. 118. tbats
9 118. 118. 118. 118. 118. tbats
10 118. 118. 118. 118. 118. tbats
# ... with 1,430 more rows, and 22 more variables: Period...7 <fct>,
# high.Lo.80 <dbl>, high.Hi.80 <dbl>, high.Lo.95 <dbl>, high.Hi.95 <dbl>,
# high.Point.Forecast <dbl>, Model...13 <fct>, Period...14 <fct>,
# low.Lo.80 <dbl>, low.Hi.80 <dbl>, low.Lo.95 <dbl>, low.Hi.95 <dbl>,
# low.Point.Forecast <dbl>, Model...20 <fct>, Period...21 <fct>,
# open.Point.Forecast <dbl>, open.Lo.80 <dbl>, open.Hi.80 <dbl>,
# open.Lo.95 <dbl>, open.Hi.95 <dbl>, Model...27 <fct>, Period...28 <fct>
> mtss[str_detect(names(mtss), 'Model.|Period.')]
# A tibble: 1,440 x 8
Model...6 Period...7 Model...13 Period...14 Model...20 Period...21 Model...27
<fct> <fct> <fct> <fct> <fct> <fct> <fct>
1 tbats dy.qt tbats dy.qt tbats dy.qt tbats
2 tbats dy.qt tbats dy.qt tbats dy.qt tbats
3 tbats dy.qt tbats dy.qt tbats dy.qt tbats
4 tbats dy.qt tbats dy.qt tbats dy.qt tbats
5 tbats dy.qt tbats dy.qt tbats dy.qt tbats
6 tbats dy.qt tbats dy.qt tbats dy.qt tbats
7 tbats dy.qt tbats dy.qt tbats dy.qt tbats
8 tbats dy.qt tbats dy.qt tbats dy.qt tbats
9 tbats dy.qt tbats dy.qt tbats dy.qt tbats
10 tbats dy.qt tbats dy.qt tbats dy.qt tbats
# ... with 1,430 more rows, and 1 more variable: Period...28 <fct>
> mtss[str_detect(names(mtss), 'Model.|Period.')] <- NULL
> mtssI used bind_cols() as alternative method to solve my issue, there has different methods depends on your issue (example: normally matrix will be more efficient than data.frame)...
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Originally posted by @englianhu in https://github.com/englianhu/report/issues/9#issuecomment-719737291