My problem goes as follows. I have the following dataset.
This is Ibex (Spanish stockmarket) ranging from 2020-01-01 to 2022-05-01
tail(ibex, 3)
| Date | Open | High | Low | Close | Adj.Close | Volume |
|---|---|---|---|---|---|---|
| 2022-04-01 | 8482 | 8878 | 8345 | 8584 | 8584 | 3824235500 |
| 2022-05-01 | 8517 | 8651 | 8139 | 8139 | 8139 | 1454670100 |
| 2022-05-10 | 8219 | 8259 | 8139 | 8139 | 8139 | 204415904 |
dim(ibex)[1] 270 7
Step 1. I select Date and Open
ibex$Date <- as.Date(ibex$Date)
ibex <- as_tibble(ibex)
ibex.1 <- ibex[,c(1,2)]
tail(ibex.1, 3)
| Date | Open |
|---|---|
| 2022-04-01 | 8482 |
| 2022-05-01 | 8517 |
| 2022-05-10 | 8219 |
Step 2. I create a ts format
ibex.1 <- ts(data = ibex.1, start = c(2000,1), end = c(2022,5), frequency = 12)
str(ibex.1)
Time-Series [1:269, 1:2] from 2000 to 2022: 10957 10988 11017 11048 11078 ...
- attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "Date" "Open"
tail(ibex.1, 3)
| Date | Open | |
|---|---|---|
| Mar 2022 | 19052 | 8462.1 |
| Apr 2022 | 19083 | 8481.7 |
| May 2022 | 19113 | 8516.6 |
Step 3. I produce a spline model using cross validation
ibex.spl <- smooth.spline(ibex.1[,1], ibex.1[,2], cv=TRUE)
Step 4. I want to predict new Open values for June, July, August 2022, i.e., values which are not in the dataset. I do this:
nuevafecha <- seq(as.Date("2022-06-01"),as.Date("2022-08-01"),by="1 month") nuevafecha <- as_tibble(nuevafecha)`
head(nuevafecha)
| value |
|---|
| 2022-06-01 |
| 2022-07-01 |
| 2022-08-01 |
Step 5. And finally this:
ibex11 <- ibex.1 %>% add_row(Date=nuevafecha$value)
l<- predict(ibex.spl, ibex.1=ibex11[270:273], se=TRUE)
tail(l$fit)
| 264 | 265 | 266 | 267 | 268 | 269 |
|---|---|---|---|---|---|
| 8354.129 | 8371.194 | 8391.543 | 8412.822 | 8439.675 | 8469.041 |
In other words: values 270:273, corresponding to June, July, August 2022 are not shown.
I just obtain predictions for the original values.
How can I get predictions for 270,271,273?
CodePudding user response：
Make sure that your x value is numeric:
# Fit model
dat$Date2 <- as.numeric(as.Date(dat$Date, format = "%Y-%m-%d"))
mymod <- smooth.spline(x = dat$Date2, y = dat$Open, cv = TRUE)
# Predict
class(mymod)
#> [1] "smooth.spline"
x_new <- as.numeric(seq(as.Date("2022-06-01"), as.Date("2022-08-01"), by = "day"))
mypreds <- predict(mymod, x = x_new)
# Visualize results
plot(mymod, xlim = c(min(dat$Date2), max(x_new)),
ylim = c(min(dat$Open), max(mypreds$y)),
xlab = "date", ylab = "open value")
points(x = dat$Date2, y = dat$Open, col = "red")
points(mypreds$x, y = mypreds$y, col = "green")
legend("topleft",
legend = c("fitted", "actual", "predicted"),
pch = c(1, 1, 1),
col = c("black", "red", "green"), inset = 0.05)
Created on 2022-06-15 by the reprex package (v2.0.1)
data:
structure(list(Date = c("2020-01-02", "2020-01-03", "2020-01-06",
"2020-01-07", "2020-01-08", "2020-01-09", "2020-01-10", "2020-01-13",
"2020-01-14", "2020-01-15", "2020-01-16", "2020-01-17", "2020-01-20",
"2020-01-21", "2020-01-22", "2020-01-23", "2020-01-24", "2020-01-27",
"2020-01-28", "2020-01-29", "2020-01-30", "2020-01-31", "2020-02-03",
"2020-02-04", "2020-02-05", "2020-02-06", "2020-02-07", "2020-02-10",
"2020-02-11", "2020-02-12", "2020-02-13", "2020-02-14", "2020-02-17",
"2020-02-18", "2020-02-19", "2020-02-20", "2020-02-21", "2020-02-24",
"2020-02-25", "2020-02-26", "2020-02-27", "2020-02-28", "2020-03-02",
"2020-03-03", "2020-03-04", "2020-03-05", "2020-03-06", "2020-03-09",
"2020-03-10", "2020-03-11", "2020-03-12", "2020-03-13", "2020-03-16",
"2020-03-17", "2020-03-18", "2020-03-19", "2020-03-20", "2020-03-23",
"2020-03-24", "2020-03-25", "2020-03-26", "2020-03-27", "2020-03-30",
"2020-03-31", "2020-04-01", "2020-04-02", "2020-04-03", "2020-04-06",
"2020-04-07", "2020-04-08", "2020-04-09", "2020-04-14", "2020-04-15",
"2020-04-16", "2020-04-17", "2020-04-20", "2020-04-21", "2020-04-22",
"2020-04-23", "2020-04-24", "2020-04-27", "2020-04-28", "2020-04-29",
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6933.299805, 6760.899902, 6729.600098, 6797.600098, 6710.799805,
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