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Handle-Missing-Value-II.Rmd
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Handle-Missing-Value-II.Rmd
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---
title: "<img src='www/binary-logo-resize.jpg' width='240'>"
subtitle: "[binary.com](https://github.com/englianhu/binary.com-interview-question) 面试试题 I - 多变量数据缺失值管理 II"
author: "[®γσ, Lian Hu（黄联富）](https://englianhu.github.io/) <img src='www/RYO.jpg' width='24'> <img src='www/RYU.jpg' width='24'> <img src='www/ENG.jpg' width='24'>®"
date: "`r lubridate::today('Asia/Tokyo')`"
output:
html_document:
number_sections: yes
toc: yes
toc_depth: 4
toc_float:
collapsed: yes
smooth_scroll: yes
code_folding: hide
---
```{r setup}
suppressPackageStartupMessages(require('BBmisc'))
## 读取程序包
pkg <- c('devtools', 'tidyverse', 'timetk', 'lubridate', 'plyr', 'dplyr', 'magrittr', 'purrr', 'stringr', 'reshape', 'formattable', 'microbenchmark', 'knitr', 'kableExtra', 'VIM', 'mice', 'miceAdds', 'mi', 'mitools', 'Amelia', 'missForest', 'Hmisc', 'DMwR', 'imputeTS', 'tidyimpute', 'mtsdi', 'xts', 'forecast', 'marima', 'missMDA')
suppressAll(lib(pkg))
funs <- c('convertOHLC.R')
l_ply(funs, function(x) source(paste0('./function/', x)))
algo <- c('interpolation', 'locf', 'mean', 'random', 'kalman', 'ma')
rm(pkg, funs)
```
# 简介
## 介绍弥补数据
由于在科研[binary.com Interview Question I - Interday High Frequency Trading Models Comparison](https://rpubs.com/englianhu/binary-Q1Inter-HFT)测试高频率量化交易时，从[fxcm/MarketData](https://github.com/fxcm/MarketData)下载的数据并不完整^[欲知更多详情，请查阅[binary.com Interview Question I - Interday High Frequency Trading Models Comparison](https://rpubs.com/englianhu/binary-Q1Inter-HFT)。]，[binary.com 面试试题 I - 单变量数据缺失值管理](http://rpubs.com/englianhu/handle-missing-value)尝试弥补缺失值不果，单变量无法辨认开市价、最高价、最低价和闭市价之间的关系。
- [How to use auto.arima to impute missing values](https://stats.stackexchange.com/questions/104565/how-to-use-auto-arima-to-impute-missing-values)使用`auto.arima()`来弥补缺失值。
- [What should be the allowed percentage of Missing Values?](https://discuss.analyticsvidhya.com/t/what-should-be-the-allowed-percentage-of-missing-values/2456)讨论着一个数据最多可以允许20%~30%的缺失值，过多的缺失值的话，该数据基本上就无法使用了。一些统计学家有本事将50%缺失值的数据复原，不过是基于许多附属变量和数据才能弥补回数据。
- [Principled Missing Data Methods for Researchers](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701793/)讲述许多弥补数据缺失值的方法与数学模式。
- [Imputation methods for time series data](https://stats.stackexchange.com/questions/261271/imputation-methods-for-time-series-data)
- [Imputing Missing Observation in Multivariate Time Series](https://stats.stackexchange.com/questions/103968/imputing-missing-observation-in-multivariate-time-series)
- [`imputeTS`: Time Series Missing Value Imputation in R](https://journal.r-project.org/archive/2017/RJ-2017-009/index.html)
- [How to Handle Missing Data](https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4)
## `impueTS`程序包
<span style='color:goldenrod'>*imputeTS - Time Series Missing Value Imputation in R*</span>讲述`mice`、`Amelia`、`missMDA`与`VIM`都是多变量弥补数据程序包，而`imputeTS`乃单变量弥补数据程序包，不过程序包中的`seadec()`函数乃弥补季节性数据。
| Simple | Imputation Imputation | Plots & Statistics | Datasets |
|:----------:|:---------------------:|:----------------------:|:-----------------:|
| na.locf | na.interpolation | plotNA.distribution | tsAirgap |
| na.mean | na.kalman | plotNA.distributionBar | tsAirgapComplete |
| na.random | na.ma | plotNA.gapsize | tsHeating |
| na.replace | na.seadec | plotNA.imputations | tsHeatingComplete |
| na.remove | na.seasplit | statsNA | tsNH4 |
| | | | tsNH4Complete |
*Table 1: General Overview imputeTS package*
| Function | Option | Description |
|:-----------------:|:-----------:|:---------------------------------------------------------------:|
| na.interpolation | linear | Imputation by Linear Interpolation |
| | spline | Imputation by Spline Interpolation |
| | stine | Imputation by Stineman Interpolation |
| | | |
| na.kalman | StructTS | Imputation by Structural Model & Kalman Smoothing |
| | auto.arima | Imputation by ARIMA State Space Representation & Kalman Sm. |
| | | |
| na.locf | locf | Imputation by Last Observation Carried Forward |
| | nocb | Imputation by Next Observation Carried Backward |
| | | |
| na.ma | simple | Missing Value Imputation by Simple Moving Average |
| | linear | Missing Value Imputation by Linear Weighted Moving Average |
| | exponential | Missing Value Imputation by Exponential Weighted Moving Average |
| | | |
| na.mean | mean | MissingValue Imputation by Mean Value |
| | median | Missing Value Imputation by Median Value |
| | mode | Missing Value Imputation by Mode Value |
| | | |
| na.random | | Missing Value Imputation by Random Sample |
| na.replace | | Replace Missing Values by a Defined Value |
| na.seadec | | Seasonally Decomposed Missing Value Imputation |
| na.seasplit | | Seasonally Splitted Missing Value Imputation |
| na.remove | | Remove Missing Values |
*Table 3: Overview Imputation Algorithms*
## `Amelia`程序包
[Amelia II: A Program for Missing Data](https://gking.harvard.edu/amelia)介绍`Amelia`程序包，而<span style='color:goldenrod'>*AMELIA II - A Program for Missing Data*</span>教导如何使用该程序包。[Error in as.POSIXct.numeric(value) : 'origin' must be supplied #18](https://github.com/IQSS/Amelia/issues/18)显示时间变量无法弥补，故此对于`Amelia`缺失值，僕得省略掉时间变量，仅设置价格变量为缺失值而已。
## 其它程序包
`mice`程序包可以使用`lm`函数将弥补数据线型化，`tidyr`程序包中有个`fill()`函数可以。而`dendextend::na_locf()`会比`zoo::na.locf()`高效率，不过弥补数据时会遇到一些参数问题。
# 数据
## 读取数据
### 1分钟数据
和之前的单变量一样，首先僕随机导入每分钟为1个时间单位的数据。
```
Error in optim(init[mask], getLike, method = "L-BFGS-B", lower = rep(0, : L-BFGS-B needs finite values of 'fn'
17. optim(init[mask], getLike, method = "L-BFGS-B", lower = rep(0, np + 1L), upper = rep(Inf, np + 1L), control = optim.control)
16. StructTS(data, ...)
15. na.kalman(data, ...)
14. apply.base.algorithm(data, algorithm = algorithm, ...)
13. .f(.x[[i]], ...)
12. map(., na.seadec, algorithm = x)
11. function_list[[i]](value)
10. freduce(value, `_function_list`)
9. `_fseq`(`_lhs`)
8. eval(quote(`_fseq`(`_lhs`)), env, env)
7. eval(quote(`_fseq`(`_lhs`)), env, env)
6. withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
5. data_m1_NA %>% dplyr::select(starts_with("Ask"), starts_with("Bid")) %>% map(na.seadec, algorithm = x) %>% as.tibble
4. FUN(X[[i]], ...)
3. lapply(pieces, .fun, ...)
2. structure(lapply(pieces, .fun, ...), dim = dim(pieces))
1. llply(algo, function(x) { data_m1_NA %>% dplyr::select(starts_with("Ask"), starts_with("Bid")) %>% map(na.seadec, algorithm = x) %>% as.tibble })
```
由于频频出现错误信息[#imputeTS/issues/26](https://github.com/SteffenMoritz/imputeTS/issues/26)，于此僕使用sort(sample(length(fls), 1))随机筛选1个文件。
```{r warning=FALSE, message=FALSE}
pth <- 'C:/Users/scibr/Documents/GitHub/scibrokes/real-time-fxcm/data/USDJPY/'
fls <- list.files(pth, pattern = '^Y[0-9]{4}W[1-9]{1,2}_m1.rds$')
## 1分钟数据
## 由于频频出现错误信息，于此僕使用sort(sample(length(fls), 1))随机筛选4个文件。
data_m1 <- llply(fls[sort(sample(length(fls), 1))], function(x) {
y <- readRDS(paste0(pth, x)) %>%
dplyr::rename(index = DateTime) %>%
mutate(index = index %>% mdy_hms %>%
.POSIXct(tz = 'Europe/Athens') %>%
force_tz())
yw <- x %>% str_extract_all('Y[0-9]{4}W[0-9]{1,2}') %>%
str_split_fixed('[A-Z]{1}', 3) %>% .[,-1]
nch <- y$index[1] %>% substr(nchar(.)+2, nchar(.)+3)
y %<>% mutate(
year = as.numeric(yw[1]), week = as.numeric(yw[2]),
nch = nch, index = if_else(
nch == '23', index + hours(1), index)) %>%
dplyr::select(-nch)
}) %>% bind_rows %>% tbl_df %>% arrange(index)
dim(data_m1)
data_m1
## 检验原始数据是否存在偏差。
data_m1 %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1 %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1) %>%
kable(caption = 'Bias Imputation') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%', height = '400px')
```
### Tick数据转为1分钟数据
接着，导入Tick数据^[欲知更多详情，请参阅[一、什么是Tick Data](https://www.fmz.com/bbs-topic/457)。]，并且转为每分钟为1时间单位。
```{r, warning=FALSE, message=FALSE}
pth <- 'C:/Users/scibr/Documents/GitHub/scibrokes/real-time-fxcm/data/USDJPY/'
fls <- list.files(pth, pattern = '^Y[0-9]{4}W[1-9]{1,2}.rds$')
## Tick数据转为1分钟数据
## 由于频频出现错误信息，于此僕使用sort(sample(length(fls), 1))随机筛选2个文件。
data_tm1 <- llply(fls[sort(sample(length(fls), 1))], function(x) {
y <- readRDS(paste0(pth, x)) %>%
convertOHLC(combine = TRUE)
yw <- x %>% str_extract_all('Y[0-9]{4}W[0-9]{1,2}') %>%
str_split_fixed('[A-Z]{1}', 3) %>% .[,-1]
y %<>% mutate(
year = as.numeric(yw[1]), week = as.numeric(yw[2]), .)
}) %>% bind_rows %>% tbl_df %>% arrange(index)
dim(data_tm1)
data_tm1
## 检验原始数据是否存在偏差。
data_tm1 %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1 %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1) %>%
kable(caption = 'Bias Imputation') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%', height = '400px')
```
## 设置缺失值
### 1分钟数据
现在尝试随机设置缺失值。
```{r warning=FALSE}
data_m1_NA <- data_m1 %>%
dplyr::select(index, BidOpen, BidHigh, BidLow, BidClose, AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.01)
data_m1_NA
data_m1_NA %>% md.pattern
data_m1_NA %>% md.pairs
```
### Tick数据转为1分钟数据
```{r warning=FALSE}
data_tm1_NA <- data_tm1 %>%
dplyr::select(index, BidOpen, BidHigh, BidLow, BidClose, AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.01)
data_tm1_NA
data_tm1_NA %>% md.pattern
data_tm1_NA %>% md.pairs
```
# 统计模式
## 弥补缺失值
- [Imputing missing data with R; MICE package](https://www.r-bloggers.com/imputing-missing-data-with-r-mice-package/)
- [mice - Multivariate Imputation by Chained Equations in R](https://github.com/englianhu/binary.com-interview-question/blob/master/reference/mice%20Multivariate%20Imputation%20by%20Chained%20Equations%20in%20R.pdf)
- [mice : Multivariate Imputation by Chained Equations](https://github.com/stefvanbuuren/mice)
- [HOW DO I PERFORM MULTIPLE IMPUTATION USING PREDICTIVE MEAN MATCHING IN R? | R FAQ](https://stats.idre.ucla.edu/r/faq/how-do-i-perform-multiple-imputation-using-predictive-mean-matching-in-r/)
- [Imputing missing observation in multivariate time series](https://stats.stackexchange.com/questions/103968/imputing-missing-observation-in-multivariate-time-series)
- [arima method in mtsdi](https://stackoverflow.com/questions/29472532/arima-method-in-mtsdi)
- [Dealing with Missing Data using R](https://medium.com/coinmonks/dealing-with-missing-data-using-r-3ae428da2d17)
- [How to use auto.arima to impute missing values](https://stats.stackexchange.com/questions/104565/how-to-use-auto-arima-to-impute-missing-values)
- [How to Fill in Missing Data in Time Series?](https://stats.stackexchange.com/questions/245615/how-to-fill-in-missing-data-in-time-series)
- [Forecasting Multivariate Data with `auto.arima`](https://stackoverflow.com/questions/15495465/forecasting-multivariate-data-with-auto-arima)
- [Multivariate Time Series Model](https://stackoverflow.com/questions/44376808/multivariate-time-series-model)
- [`auto.arima` using `xreg` and Forecasting Several ts Together](https://stackoverflow.com/questions/25036986/auto-arima-using-xreg-and-forecasting-several-ts-together)
- [`auto.arima` Forecast with Multivariate `xreg` - unexpected Results](https://stackoverflow.com/questions/15054800/auto-arima-forecast-with-multivariate-xreg-unexpected-results)
- [`auto.arima` Warns `NaNs` Produced on Std Error](https://stats.stackexchange.com/questions/26999/auto-arima-warns-nans-produced-on-std-error)
- [Arima time series forecast (auto.arima) with multiple exogeneous variables in R](https://stats.stackexchange.com/questions/122803/arima-time-series-forecast-auto-arima-with-multiple-exogeneous-variables-in-r)
- [Multivariate ARIMA with regression](https://stats.stackexchange.com/questions/45993/multivariate-arima-with-regression)
- [I am trying to do a multivariate time series analysis on r. how to use auto.arima with Xreg?](https://www.researchgate.net/post/I_am_trying_to_do_a_multivariate_time_series_analysis_on_r_how_to_use_autoarima_with_Xreg)
```{r warning=FALSE}
tttt <- data_m1_NA[-1] %>% amelia
llply(tttt$imputations, function(x) {
x %>% mutate(
VA = if_else(AskOpen <= AskHigh & AskOpen >= AskLow &
AskClose <= AskHigh & AskClose >= AskLow &
AskHigh >= AskLow, 1, 0),
VB = if_else(BidOpen <= BidHigh & BidOpen >= BidLow &
BidClose <= BidHigh & BidClose >= BidLow &
BidHigh >= BidLow, 1, 0)) %>%
dplyr::filter(VA == 0|VB == 0)
})
```
经过测试以上数据，结果发现`amelia`也是单变量数据弥补。
**注释：单变量弥补的数据将会与之前单变量预测数据一样，就是出现偏差，例如：**
- 开市价高于最高价
- 开市价低于最低价
- 最高价低于开市价
- 最高价低于最低价
- 最高价低于闭市价
- 最低价高于开市价
- 最低价高于最高价
- 最低价高于闭市价
- 闭市价高于最高价
- 闭市价低于最低价
## 1% 缺失值
### 1分钟数据
以下使用`imputeTS::na.seadec()`弥补1%数据缺失值。
```{r warning=FALSE}
data_m1_NA <- data_m1 %>%
dplyr::select(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.01) %>%
cbind(data_m1[1], .) %>% tbl_df
data_m1_1_impTS <- llply(algo, function(x) {
data_m1_NA %>%
dplyr::select(starts_with('Ask'), starts_with('Bid')) %>%
map(na.seadec, algorithm = x) %>% as.tibble
})
names(data_m1_1_impTS) <- algo
data_m1_1_impTS %<>% ldply %>% tbl_df
data_m1_1_impTS %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_1_impTS %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_1_impTS %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_1_impTS %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`Amelia::amelia()`弥补1%数据缺失值。
```{r warning=FALSE}
data_m1_1_amelia <- data_m1_NA %>%
amelia %>%
.$imputations %>%
ldply %>% tbl_df
data_m1_1_amelia %>% anyNA
data_m1_1_amelia %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_1_amelia %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_1_amelia %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_1_amelia %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`tidyr::fill()`弥补1%数据缺失值。
```{r warning=FALSE}
data_m1_1_tidyr <- data_m1_NA %>%
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>% #default direction down
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose, .direction = 'up')
data_m1_1_tidyr %>% anyNA
data_m1_1_tidyr %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_1_tidyr %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_1_tidyr %<>%
summarise(
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_1_tidyr %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
### Tick数据转为1分钟数据
以下使用`imputeTS::na.seadec()`弥补1%数据缺失值。
```{r warning=FALSE}
data_tm1_NA <- data_tm1 %>%
dplyr::select(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.01) %>%
cbind(data_tm1[1], .) %>% tbl_df
data_tm1_1_impTS <- llply(algo, function(x) {
data_tm1_NA %>%
dplyr::select(starts_with('Ask'), starts_with('Bid')) %>%
map(na.seadec, algorithm = x) %>% as.tibble
})
names(data_tm1_1_impTS) <- algo
data_tm1_1_impTS %<>% ldply %>% tbl_df
data_tm1_1_impTS %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_1_impTS %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_1_impTS %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_1_impTS %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`Amelia::amelia()`弥补1%数据缺失值。
```{r warning=FALSE}
data_tm1_1_amelia <- data_tm1_NA %>%
amelia %>%
.$imputations %>%
ldply %>% tbl_df
data_tm1_1_amelia %>% anyNA
data_tm1_1_amelia %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_1_amelia %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_1_amelia %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_1_amelia %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`tidyr::fill()`弥补1%数据缺失值。
```{r warning=FALSE}
data_tm1_1_tidyr <- data_tm1_NA %>%
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>% #default direction down
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose, .direction = 'up')
data_tm1_1_tidyr %>% anyNA
data_tm1_1_tidyr %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_1_tidyr %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_1_tidyr %<>%
summarise(
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_1_tidyr %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
## 10% 缺失值
### 1分钟数据
以下使用`imputeTS::na.seadec()`弥补10%数据缺失值。
```{r warning=FALSE}
data_m1_NA <- data_m1 %>%
dplyr::select(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.1) %>%
cbind(data_m1[1], .) %>% tbl_df
data_m1_10_impTS <- llply(algo, function(x) {
data_m1_NA %>%
dplyr::select(starts_with('Ask'), starts_with('Bid')) %>%
map(na.seadec, algorithm = x) %>% as.tibble
})
names(data_m1_10_impTS) <- algo
data_m1_10_impTS %<>% ldply %>% tbl_df
data_m1_10_impTS %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_10_impTS %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_10_impTS %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_10_impTS %>%
kable(caption = 'MSE 10% 缺失值') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`Amelia::amelia()`弥补10%数据缺失值。
```{r warning=FALSE}
data_m1_10_amelia <- data_m1_NA %>%
amelia %>%
.$imputations %>%
ldply %>% tbl_df
data_m1_10_amelia %>% anyNA
data_m1_10_amelia %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_10_amelia %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_10_amelia %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_10_amelia %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`tidyr::fill()`弥补10%数据缺失值。
```{r warning=FALSE}
data_m1_10_tidyr <- data_m1_NA %>%
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>% #default direction down
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose, .direction = 'up')
data_m1_10_tidyr %>% anyNA
data_m1_10_tidyr %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_10_tidyr %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_10_tidyr %<>%
summarise(
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_10_tidyr %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
### Tick数据转为1分钟数据
以下使用`imputeTS::na.seadec()`弥补10%数据缺失值。
```{r warning=FALSE}
data_tm1_NA <- data_tm1 %>%
dplyr::select(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.1) %>%
cbind(data_tm1[1], .) %>% tbl_df
data_tm1_10_impTS <- llply(algo, function(x) {
data_tm1_NA %>%
dplyr::select(starts_with('Ask'), starts_with('Bid')) %>%
map(na.seadec, algorithm = x) %>% as.tibble
})
names(data_tm1_10_impTS) <- algo
data_tm1_10_impTS %<>% ldply %>% tbl_df
data_tm1_10_impTS %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_10_impTS %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_10_impTS %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_10_impTS %>%
kable(caption = 'MSE 10% 缺失值') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`Amelia::amelia()`弥补10%数据缺失值。
```{r warning=FALSE}
data_tm1_10_amelia <- data_tm1_NA %>%
amelia %>%
.$imputations %>%
ldply %>% tbl_df
data_tm1_10_amelia %>% anyNA
data_tm1_10_amelia %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_10_amelia %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_10_amelia %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_10_amelia %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`tidyr::fill()`弥补10%数据缺失值。
```{r warning=FALSE}
data_tm1_10_tidyr <- data_tm1_NA %>%
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>% #default direction down
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose, .direction = 'up')
data_tm1_10_tidyr %>% anyNA
data_tm1_10_tidyr %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_10_tidyr %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_10_tidyr %<>%
summarise(
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_10_tidyr %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
## 20% 缺失值
### 1分钟数据
以下使用`imputeTS::na.seadec()`弥补20%数据缺失值。
```{r warning=FALSE}
data_m1_NA <- data_m1 %>%
dplyr::select(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.2) %>%
cbind(data_m1[1], .) %>% tbl_df
data_m1_20_impTS <- llply(algo, function(x) {
data_m1_NA %>%
dplyr::select(starts_with('Ask'), starts_with('Bid')) %>%
map(na.seadec, algorithm = x) %>% as.tibble
})
names(data_m1_20_impTS) <- algo
data_m1_20_impTS %<>% ldply %>% tbl_df
data_m1_20_impTS %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_20_impTS %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_20_impTS %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_20_impTS %>%
kable(caption = 'MSE 20% 缺失值') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`Amelia::amelia()`弥20%数据缺失值。
```{r warning=FALSE}
data_m1_20_amelia <- data_m1_NA %>%
amelia %>%
.$imputations %>%
ldply %>% tbl_df
data_m1_20_amelia %>% anyNA
data_m1_20_amelia %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_20_amelia %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_20_amelia %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_20_amelia %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`tidyr::fill()`弥补20%数据缺失值。
```{r warning=FALSE}
data_m1_20_tidyr <- data_m1_NA %>%
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>% #default direction down
fill(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose, .direction = 'up')
data_m1_20_tidyr %>% anyNA
data_m1_20_tidyr %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_m1_20_tidyr %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_m1_20_tidyr %<>%
summarise(
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_m1_20_tidyr %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
### Tick数据转为1分钟数据
以下使用`imputeTS::na.seadec()`弥补20%数据缺失值。
```{r warning=FALSE}
data_tm1_NA <- data_tm1 %>%
dplyr::select(BidOpen, BidHigh, BidLow, BidClose,
AskOpen, AskHigh, AskLow, AskClose) %>%
prodNA(noNA = 0.2) %>%
cbind(data_tm1[1], .) %>% tbl_df
data_tm1_20_impTS <- llply(algo, function(x) {
data_tm1_NA %>%
dplyr::select(starts_with('Ask'), starts_with('Bid')) %>%
map(na.seadec, algorithm = x) %>% as.tibble
})
names(data_tm1_20_impTS) <- algo
data_tm1_20_impTS %<>% ldply %>% tbl_df
data_tm1_20_impTS %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_20_impTS %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_20_impTS %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_20_impTS %>%
kable(caption = 'MSE 20% 缺失值') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`Amelia::amelia()`弥补20%数据缺失值。
```{r warning=FALSE}
data_tm1_20_amelia <- data_tm1_NA %>%
amelia %>%
.$imputations %>%
ldply %>% tbl_df
data_tm1_20_amelia %>% anyNA
data_tm1_20_amelia %<>% mutate(
bias.open = if_else(AskOpen>AskHigh|AskOpen<AskLow, 1, 0),
bias.high = if_else(AskHigh<AskOpen|AskHigh<AskLow|AskHigh<AskClose, 1, 0),
bias.low = if_else(AskLow>AskOpen|AskLow>AskHigh|AskLow>AskClose, 1, 0),
bias.close = if_else(AskClose>AskHigh|AskClose<AskLow, 1, 0))
data_tm1_20_amelia %>%
dplyr::filter(bias.open==1|bias.high==1|bias.low==1|bias.close==1)
data_tm1_20_amelia %<>%
ddply(.(.id), summarise,
AskOpen = mean((AskOpen - data_m1$AskOpen)^2),
AskHigh = mean((AskHigh - data_m1$AskHigh)^2),
AskLow = mean((AskLow - data_m1$AskLow)^2),
AskClose = mean((AskClose - data_m1$AskClose)^2),
Mean.HLC = (AskHigh + AskLow + AskClose)/3,
Mean.OHLC = (AskOpen + AskHigh + AskLow + AskClose)/4,
bias.open = sum(bias.open)/length(bias.open),
bias.high = sum(bias.high)/length(bias.high),
bias.low = sum(bias.low)/length(bias.low),
bias.close = sum(bias.close)/length(bias.close)) %>% tbl_df
data_tm1_20_amelia %>%
kable(caption = 'MSE') %>%
kable_styling(bootstrap_options = c('striped', 'hover', 'condensed', 'responsive')) %>%
scroll_box(width = '100%')#, height = '400px')
```
以下使用`tidyr::fill()`弥补20%数据缺失值。
```{r warning=FALSE}
data_tm1_20_tidyr <- data_tm1_NA %>%