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python - 按分区循环自动 Arima

问题描述

我正在合作一个项目，该项目需要我使用迄今为止我没有任何经验的 R。我正在尝试将 auto arima 应用于我的数据集中的分区/窗口，但我对如何开始一无所知。3

本质上，我想使用行 c_id = "none" 在每个 partner_id 上训练一个单独的模型，然后预测/预测每个 partner_id 的最大值（日期）。每个合作伙伴的月数/行数长度不同。对于下面粘贴的这个示例数据框，partner_id = "1A9" 有 12 个月/行，c_id = "none" 而 partner_id = "1B9" 有 13 个月/行，c_id = "none"。每个 partner_is 中扩展到 max(Date) 的月数/行数也各不相同。这很棘手，因为我假设我需要动态输入要训练的月数/行数以及要预测每个 partner_id 的月数/行数。

我在下面包含了一个示例数据集。

x <- data.frame("c_id" = c("none","none","none","none","none",
"none","none","none","none","none","none","none","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101", "none","none","none","none","none","none","none","none","none","none","none","none","none","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111"), "partner_id" = c("1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9"), "rev_month" = as.Date(c("2016-01-01","2016-01-01","2016-02-01","2016-03-01","2016-04-01","2016-05-01","2016-06-01","2016-07-01","2016-08-01", "2016-09-01","2016-10-01","2016-11-01","2016-12-01","2017-01-01","2017-02-01","2017-03-01","2017-04-01","2017-05-01","2017-06-01","2017-07-01","2017-08-01","2017-09-01","2017-10-01","2017-11-01","2017-12-01","2018-01-01","2018-02-01","2018-03-01","2018-04-01","2018-05-01","2018-06-01","2018-07-01","2018-08-01","2018-09-01","2018-10-01","2018-11-01","2018-12-01", "2017-01-01","2017-01-01","2017-02-01","2017-03-01","2017-04-01","2017-05-01","2017-06-01","2017-07-01","2017-08-01", "2017-09-01","2017-10-01","2017-11-01","2017-12-01","2018-01-01","2018-02-01","2018-03-01","2018-04-01","2018-05-01","2018-06-01","2018-07-01","2018-08-01","2018-09-01","2018-10-01","2018-11-01","2018-12-01","2019-01-01","2019-02-01","2019-03-01","2019-04-01","2019-05-01","2019-06-01","2019-07-01","2019-08-01","2019-09-01","2019-10-01","2019-11-01","2019-12-01", "2020-01-01", "2020-02-01", "2020-03-01")), "rev" = c(101.25, 102.25, 103.50, 103.75, 104.15, 104.25, 104.3, 105.00, 105.20, 105.60, 106.00, 106.10, 106.50, 101.50, 100.30, 107.50, 108.30, 108.45, 109.10, 110.10, 112.15, 112.45, 114.65, 115.00, 116.00, 116.50, 117.25, 117.85, 119.25, 119.95, 120.20, 121.50, 122.30, 122.40, 123.25, 123.75, 124.00, 101.25, 102.25, 103.50, 103.75, 104.15, 104.25, 104.3, 105.00, 105.20, 105.60, 106.00, 106.10, 106.50, 101.50, 100.30, 107.50, 108.30, 108.45, 109.10, 110.10, 112.15, 112.45, 114.65, 115.00, 116.00, 116.50, 117.25, 117.85, 119.25, 119.95, 120.20, 121.50, 122.30, 122.40, 123.25, 123.75, 124.00, 124.10, 125.35, 125.45), stingsAsFactors=FALSE)

我很抱歉还没有任何代码起始代码，因为我仍在尝试从概念上考虑这一点，而对 R 没有太多经验。最终，我想将预测和置信区间列添加回我的原始数据框。我愿意接受任何 R 和/或 Python 解决方案。

标签： pythonrfor-looparima

解决方案

从关于 R 和时间序列的编程角度来看，我的回答在很多层面上都是错误的。主要方面是（还有其他问题，但我知道您的担心是让它尽快工作）：

  1. 首先应该避免循环 - 但我的猜测是，矢量化解决方案会让你更难理解

  2. 如果您正在寻找季节性模式，则将 arima 用于至少没有两个完整周期（在这种情况下为年）的时间序列并不是很有希望。

如果您真的对 R 中的时间序列预测主题感兴趣，请阅读这本书：https ://otexts.com/fpp2/

一个相关的附带问题是您的测试数据：合作伙伴的两个系列在第一个和第二个位置都有重复的日期，这与固定周期/间隔的时间序列预测不符 - 我只是落后于第一个以使事情正常进行。因此新的训练数据是这样的（我们不需要 stringsAsFactores=FALSE）：

 x <- data.frame(c_id = c("none","none","none","none","none","none","none","none","none","none","none","none","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-100","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101","c-101", "none","none","none","none","none","none","none","none","none","none","none","none","none","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-110","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111","c-111"), "partner_id" = c("1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1A9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9","1B9"),
                rev_month = as.Date(c("2015-12-01","2016-01-01","2016-02-01","2016-03-01","2016-04-01","2016-05-01","2016-06-01","2016-07-01","2016-08-01", "2016-09-01","2016-10-01","2016-11-01","2016-12-01","2017-01-01","2017-02-01","2017-03-01","2017-04-01","2017-05-01","2017-06-01","2017-07-01","2017-08-01","2017-09-01","2017-10-01","2017-11-01","2017-12-01","2018-01-01","2018-02-01","2018-03-01","2018-04-01","2018-05-01","2018-06-01","2018-07-01","2018-08-01","2018-09-01","2018-10-01","2018-11-01","2018-12-01", "2016-12-31","2017-01-01","2017-02-01","2017-03-01","2017-04-01","2017-05-01","2017-06-01","2017-07-01","2017-08-01", "2017-09-01","2017-10-01","2017-11-01","2017-12-01","2018-01-01","2018-02-01","2018-03-01","2018-04-01","2018-05-01","2018-06-01","2018-07-01","2018-08-01","2018-09-01","2018-10-01","2018-11-01","2018-12-01","2019-01-01","2019-02-01","2019-03-01","2019-04-01","2019-05-01","2019-06-01","2019-07-01","2019-08-01","2019-09-01","2019-10-01","2019-11-01","2019-12-01", "2020-01-01", "2020-02-01", "2020-03-01")),
                rev = c(101.25, 102.25, 103.50, 103.75, 104.15, 104.25, 104.3, 105.00, 105.20, 105.60, 106.00, 106.10, 106.50, 101.50, 100.30, 107.50, 108.30, 108.45, 109.10, 110.10, 112.15, 112.45, 114.65, 115.00, 116.00, 116.50, 117.25, 117.85, 119.25, 119.95, 120.20, 121.50, 122.30, 122.40, 123.25, 123.75, 124.00, 101.25, 102.25, 103.50, 103.75, 104.15, 104.25, 104.3, 105.00, 105.20, 105.60, 106.00, 106.10, 106.50, 101.50, 100.30, 107.50, 108.30, 108.45, 109.10, 110.10, 112.15, 112.45, 114.65, 115.00, 116.00, 116.50, 117.25, 117.85, 119.25, 119.95, 120.20, 121.50, 122.30, 122.40, 123.25, 123.75, 124.00, 124.10, 125.35, 125.45))

现在我们设置了一个 data.frame 来存储预测——尽管这在理论上是不正确的（“永远不要增长一个向量”）并且有更好的解决方案但是它会使它变得更加复杂并且无助于理解实现：

# empty data.frame to fill in predictions
predictions_df <- data.frame(c_id=character(),
                             partner=character(),
                             rev_month = character(),
                             rev=double())

现在我们建立一个独特的合作伙伴向量来循环：

# unique partners
partners <- unique(x$partner_id)

让我们调用本练习所需的库：

library(xts)
library(dplyr)
library(forecast)

主要部分是循环本身：

# loop to build predictions and store them
for (i in 1:length(partners)){

  partner <- partners[i] # get specific partner
  x1 <- x[x$partner_id == partner, ] # get data for specific partner
  x1_t <- x1[x1$c_id == "none", c(3,4)] # training data
  x1_f <- x1[x1$c_id != "none", c(3,4)] # forecast data
  c_id <- x1[x1$c_id != "none", 1] # complementary data

  # convert training data to time-series object
  x1_t_ts <- xts(x1_t[,-1], order.by=as.Date(x1_t[,1], "%Y/%m/%d"))
  # run auto arima on the time series
  tm <- forecast::auto.arima(x1_t_ts)
  # forecast the number of future steps (rows for to predict data)
  fc <- forecast::forecast(tm, nrow(x1_f))

  predictions_df <- rbind(predictions_df, data.frame(c_id, partner, rev_month = as.character(x1_f$rev_month), rev = as.double(fc$mean)))

}

最后让我们看看结果：

predictions_df

    c_id partner  rev_month      rev
1  c-100     1A9 2016-12-01 106.5409
2  c-100     1A9 2017-01-01 106.9818
3  c-100     1A9 2017-02-01 107.4227
4  c-100     1A9 2017-03-01 107.8636
5  c-100     1A9 2017-04-01 108.3045
6  c-100     1A9 2017-05-01 108.7455
7  c-100     1A9 2017-06-01 109.1864
8  c-100     1A9 2017-07-01 109.6273
9  c-100     1A9 2017-08-01 110.0682
10 c-100     1A9 2017-09-01 110.5091
11 c-100     1A9 2017-10-01 110.9500
12 c-100     1A9 2017-11-01 111.3909
13 c-101     1A9 2017-12-01 111.8318
14 c-101     1A9 2018-01-01 112.2727
15 c-101     1A9 2018-02-01 112.7136
16 c-101     1A9 2018-03-01 113.1545
17 c-101     1A9 2018-04-01 113.5955
18 c-101     1A9 2018-05-01 114.0364
19 c-101     1A9 2018-06-01 114.4773
20 c-101     1A9 2018-07-01 114.9182
21 c-101     1A9 2018-08-01 115.3591
22 c-101     1A9 2018-09-01 115.8000
23 c-101     1A9 2018-10-01 116.2409
24 c-101     1A9 2018-11-01 116.6818
25 c-101     1A9 2018-12-01 117.1227
26 c-110     1B9 2018-01-01 106.9375
27 c-110     1B9 2018-02-01 107.3750
28 c-110     1B9 2018-03-01 107.8125
29 c-110     1B9 2018-04-01 108.2500
30 c-110     1B9 2018-05-01 108.6875
31 c-110     1B9 2018-06-01 109.1250
32 c-110     1B9 2018-07-01 109.5625
33 c-110     1B9 2018-08-01 110.0000
34 c-110     1B9 2018-09-01 110.4375
35 c-110     1B9 2018-10-01 110.8750
36 c-110     1B9 2018-11-01 111.3125
37 c-110     1B9 2018-12-01 111.7500
38 c-111     1B9 2019-01-01 112.1875
39 c-111     1B9 2019-02-01 112.6250
40 c-111     1B9 2019-03-01 113.0625
41 c-111     1B9 2019-04-01 113.5000
42 c-111     1B9 2019-05-01 113.9375
43 c-111     1B9 2019-06-01 114.3750
44 c-111     1B9 2019-07-01 114.8125
45 c-111     1B9 2019-08-01 115.2500
46 c-111     1B9 2019-09-01 115.6875
47 c-111     1B9 2019-10-01 116.1250
48 c-111     1B9 2019-11-01 116.5625
49 c-111     1B9 2019-12-01 117.0000
50 c-111     1B9 2020-01-01 117.4375
51 c-111     1B9 2020-02-01 117.8750
52 c-111     1B9 2020-03-01 118.3125

如果您想获得置信区间等，请解构循环（仅使用“i <- 1”运行内部部分）并了解发生了什么以及返回值是什么。那么使用我提供的shemata来获得你需要的东西应该没有问题。

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