[英]Forecasting irregular stock data with ARIMA and tsibble
I want to forecast a certain stock using ARIMA in a similar way that R. Hyndman does it in FPP3 .我想以与 R. Hyndman 在FPP3中类似的方式使用 ARIMA 预测某只股票。
The first issue that I've run into is that stock data is obviously irregular, since the stock exchange is closed during weekends and some holidays.我遇到的第一个问题是股票数据明显不规则,因为证券交易所在周末和一些节假日休市。 This creates some issues if I want to use functions from the tidyverts packages:
如果我想使用 tidyverts 包中的函数,这会产生一些问题:
> stock
# A tsibble: 750 x 6 [1D]
Date Open High Low Close Volume
<date> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2019-05-21 36.3 36.4 36.3 36.4 232
2 2019-05-22 36.4 37.0 36.4 36.8 1007
3 2019-05-23 36.7 36.8 36.1 36.1 4298
4 2019-05-24 36.4 36.5 36.4 36.4 452
5 2019-05-27 36.5 36.5 36.3 36.4 2032
6 2019-05-28 36.5 36.8 36.4 36.5 3049
7 2019-05-29 36.2 36.5 36.1 36.5 2962
8 2019-05-30 36.8 37.1 36.8 37.1 432
9 2019-05-31 36.8 37.4 36.8 37.4 8424
10 2019-06-03 37.3 37.5 37.2 37.3 1550
# ... with 740 more rows
> stock %>%
+ feasts::ACF(difference(Close)) %>%
+ autoplot()
Error in `check_gaps()`:
! .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using `tsibble::fill_gaps()` if required.
The same error regarding gaps in time applies to other functions like fable::ARIMA() or feasts::gg_tsdisplay().关于时间间隔的相同错误适用于其他函数,如 fable::ARIMA() 或 feasts::gg_tsdisplay()。
I have tried filling the gaps with values from previous rows:我尝试用前几行的值填补空白:
stock %>%
group_by_key() %>%
fill_gaps() %>%
tidyr::fill(Close, .direction = "down")
# A tsibble: 1,096 x 6 [1D]
Date Open High Low Close Volume
<date> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2019-05-21 36.3 36.4 36.3 36.4 232
2 2019-05-22 36.4 37.0 36.4 36.8 1007
3 2019-05-23 36.7 36.8 36.1 36.1 4298
4 2019-05-24 36.4 36.5 36.4 36.4 452
5 2019-05-25 NA NA NA 36.4 NA
6 2019-05-26 NA NA NA 36.4 NA
7 2019-05-27 36.5 36.5 36.3 36.4 2032
8 2019-05-28 36.5 36.8 36.4 36.5 3049
9 2019-05-29 36.2 36.5 36.1 36.5 2962
10 2019-05-30 36.8 37.1 36.8 37.1 432
# ... with 1,086 more rows
and everything works as it should from there.从那里开始一切正常。 My question is:
我的问题是:
First, you're clearly using an old version of the feasts package, because the current version gives a warning rather than an error when computing the ACF from data with implicit gaps.首先,您显然使用的是旧版本的 feasts 包,因为当前版本在从具有隐式间隙的数据计算 ACF 时会发出警告而不是错误。
Second, the answer depends on what analysis you want to do.其次,答案取决于您要进行的分析。 You have three choices:
你有三个选择:
Here are the results for each of them, using an example of Apple stock over the period 2014-2018.以下是他们每个人的结果,以 2014-2018 年期间的苹果股票为例。
library(fpp3)
#> ── Attaching packages ─────────────────────────────────────── fpp3 0.4.0.9000 ──
#> ✔ tibble 3.1.7 ✔ tsibble 1.1.1
#> ✔ dplyr 1.0.9 ✔ tsibbledata 0.4.0
#> ✔ tidyr 1.2.0 ✔ feasts 0.2.2
#> ✔ lubridate 1.8.0 ✔ fable 0.3.1
#> ✔ ggplot2 3.3.6 ✔ fabletools 0.3.2
#> ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
#> ✖ lubridate::date() masks base::date()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ tsibble::intersect() masks base::intersect()
#> ✖ tsibble::interval() masks lubridate::interval()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ tsibble::setdiff() masks base::setdiff()
#> ✖ tsibble::union() masks base::union()
stock <- gafa_stock %>%
filter(Symbol == "AAPL") %>%
tsibble(index = Date, regular = TRUE) %>%
fill_gaps()
stock
#> # A tsibble: 1,825 x 8 [1D]
#> Symbol Date Open High Low Close Adj_Close Volume
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AAPL 2014-01-02 79.4 79.6 78.9 79.0 67.0 58671200
#> 2 AAPL 2014-01-03 79.0 79.1 77.2 77.3 65.5 98116900
#> 3 <NA> 2014-01-04 NA NA NA NA NA NA
#> 4 <NA> 2014-01-05 NA NA NA NA NA NA
#> 5 AAPL 2014-01-06 76.8 78.1 76.2 77.7 65.9 103152700
#> 6 AAPL 2014-01-07 77.8 78.0 76.8 77.1 65.4 79302300
#> 7 AAPL 2014-01-08 77.0 77.9 77.0 77.6 65.8 64632400
#> 8 AAPL 2014-01-09 78.1 78.1 76.5 76.6 65.0 69787200
#> 9 AAPL 2014-01-10 77.1 77.3 75.9 76.1 64.5 76244000
#> 10 <NA> 2014-01-11 NA NA NA NA NA NA
#> # … with 1,815 more rows
stock %>%
model(ARIMA(Close ~ pdq(d=1)))
#> A mable: 1 x 1
#> `ARIMA(Close ~ pdq(d = 1))`
#> <model>
#> 1 <ARIMA(0,1,0)>
In this case, calculations of the ACF will find the longest contiguous part which is too small to be meaningful, so there isn't any point showing the results of ACF()
or gg_tsdisplay()
.在这种情况下,ACF 的计算将找到最长的连续部分,该部分太小而无意义,因此没有任何点显示
ACF()
或gg_tsdisplay()
的结果。 Also, the automated choice of differencing in the ARIMA model fails due to the missing values, so I have manually set it to one.此外,由于缺少值,ARIMA 模型中差分的自动选择失败,因此我手动将其设置为 1。 The other parts of the ARIMA model work fine in the presence of missing values.
ARIMA 模型的其他部分在存在缺失值的情况下工作正常。
stock <- stock %>%
tidyr::fill(Close, .direction = "down")
stock
#> # A tsibble: 1,825 x 8 [1D]
#> Symbol Date Open High Low Close Adj_Close Volume
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AAPL 2014-01-02 79.4 79.6 78.9 79.0 67.0 58671200
#> 2 AAPL 2014-01-03 79.0 79.1 77.2 77.3 65.5 98116900
#> 3 <NA> 2014-01-04 NA NA NA 77.3 NA NA
#> 4 <NA> 2014-01-05 NA NA NA 77.3 NA NA
#> 5 AAPL 2014-01-06 76.8 78.1 76.2 77.7 65.9 103152700
#> 6 AAPL 2014-01-07 77.8 78.0 76.8 77.1 65.4 79302300
#> 7 AAPL 2014-01-08 77.0 77.9 77.0 77.6 65.8 64632400
#> 8 AAPL 2014-01-09 78.1 78.1 76.5 76.6 65.0 69787200
#> 9 AAPL 2014-01-10 77.1 77.3 75.9 76.1 64.5 76244000
#> 10 <NA> 2014-01-11 NA NA NA 76.1 NA NA
#> # … with 1,815 more rows
stock %>%
ACF(difference(Close)) %>%
autoplot()
stock %>%
model(ARIMA(Close))
#> # A mable: 1 x 1
#> `ARIMA(Close)`
#> <model>
#> 1 <ARIMA(0,1,0)>
stock %>%
gg_tsdisplay(Close)
stock <- gafa_stock %>%
filter(Symbol == "AAPL") %>%
tsibble(index = Date, regular = TRUE) %>%
mutate(trading_day = row_number()) %>%
tsibble(index = trading_day)
stock
#> # A tsibble: 1,258 x 9 [1]
#> Symbol Date Open High Low Close Adj_Close Volume trading_day
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 AAPL 2014-01-02 79.4 79.6 78.9 79.0 67.0 58671200 1
#> 2 AAPL 2014-01-03 79.0 79.1 77.2 77.3 65.5 98116900 2
#> 3 AAPL 2014-01-06 76.8 78.1 76.2 77.7 65.9 103152700 3
#> 4 AAPL 2014-01-07 77.8 78.0 76.8 77.1 65.4 79302300 4
#> 5 AAPL 2014-01-08 77.0 77.9 77.0 77.6 65.8 64632400 5
#> 6 AAPL 2014-01-09 78.1 78.1 76.5 76.6 65.0 69787200 6
#> 7 AAPL 2014-01-10 77.1 77.3 75.9 76.1 64.5 76244000 7
#> 8 AAPL 2014-01-13 75.7 77.5 75.7 76.5 64.9 94623200 8
#> 9 AAPL 2014-01-14 76.9 78.1 76.8 78.1 66.1 83140400 9
#> 10 AAPL 2014-01-15 79.1 80.0 78.8 79.6 67.5 97909700 10
#> # … with 1,248 more rows
stock %>%
ACF(difference(Close)) %>%
autoplot()
stock %>%
model(ARIMA(Close))
#> # A mable: 1 x 1
#> `ARIMA(Close)`
#> <model>
#> 1 <ARIMA(2,1,3)>
stock %>%
gg_tsdisplay(Close)
Created on 2022-05-22 by the reprex package (v2.0.1)由reprex 包于 2022-05-22 创建 (v2.0.1)
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