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我正在使用fableandfabletools包进行一些时间序列预测分析，并且我有兴趣比较单个模型和混合模型（由我正在使用的单个模型组成）的准确性。

这是一些带有模拟数据框的示例代码：-

library(fable)
library(fabletools)
library(distributional)
library(tidyverse)
library(imputeTS)

#creating mock dataframe
set.seed(1)  

Date<-seq(as.Date("2018-01-01"), as.Date("2021-03-19"), by = "1 day")

Count<-rnorm(length(Date),mean = 2086, sd= 728)

Count<-round(Count)

df<-data.frame(Date,Count)

df

#===================redoing with new model================

df$Count<-abs(df$Count)#in case there is any negative values, force them to be absolute

count_data<-as_tsibble(df)

count_data<-imputeTS::na.mean(count_data)

testfrac<-count_data%>%arrange(Date)%>%sample_frac(0.8)
lastdate<-last(testfrac$Date)

#train data
train <- count_data %>%
  #sample_frac(0.8)
  filter(Date<=as.Date(lastdate))
set.seed(1)
fit <- train %>%
  model(
    ets = ETS(Count),
    arima = ARIMA(Count),
    snaive = SNAIVE(Count),
    croston= CROSTON(Count),
    ave=MEAN(Count),
    naive=NAIVE(Count),
    neural=NNETAR(Count),
    lm=TSLM(Count ~ trend()+season())
  ) %>%
  mutate(mixed = (ets + arima + snaive + croston + ave + naive + neural + lm) /8)# creates a combined model using the averages of all individual models 


fc <- fit %>% forecast(h = 7)

accuracy(fc,count_data)

fc_accuracy <- accuracy(fc, count_data,
                        measures = list(
                          point_accuracy_measures,
                          interval_accuracy_measures,
                          distribution_accuracy_measures
                        )
)

fc_accuracy

# A tibble: 9 x 13
#  .model  .type     ME  RMSE   MAE   MPE  MAPE  MASE RMSSE   ACF1 winkler percentile  CRPS
#  <chr>   <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>      <dbl> <dbl>
#1 arima   Test  -191.   983.  744. -38.1  51.8 0.939 0.967 -0.308   5769.       567.  561.
#2 ave     Test  -191.   983.  744. -38.1  51.8 0.939 0.967 -0.308   5765.       566.  561.
#3 croston Test  -191.   983.  745. -38.2  51.9 0.940 0.968 -0.308  29788.       745.  745.
#4 ets     Test  -189.   983.  743. -38.0  51.7 0.938 0.967 -0.308   5759.       566.  560.
#5 lm      Test  -154.  1017.  742. -36.5  51.1 0.937 1.00  -0.307   6417.       583.  577.
#6 mixed   Test  -173.   997.  747. -36.8  51.1 0.944 0.981 -0.328  29897.       747.  747.
#7 naive   Test    99.9  970.  612. -19.0  38.7 0.772 0.954 -0.308   7856.       692.  685.
#8 neural  Test  -322.  1139.  934. -49.6  66.3 1.18  1.12  -0.404  26361.       852.  848.
#9 snaive  Test  -244   1192.  896. -37.1  55.5 1.13  1.17  -0.244   4663.       690.  683.

我演示了如何创建混合模型。但是，可能有一些单独的模型在添加到混合模型时会妨碍其性能；换句话说，如果混合模型不包括以有害方式扭曲准确性的单个模型，则混合模型可能会得到改进。

期望的结果

我想要实现的是能够测试单个模型的所有可能组合，并返回在其中一个准确度指标上具有最佳性能的混合模型，例如平均绝对误差 (MAE)。但我不确定如何以自动化方式执行此操作，因为有很多潜在的组合。

有人可以建议或分享一些关于我如何做到这一点的代码吗？

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1 回答 1

2

有几点需要考虑：

  • 虽然快速评估许多组合模型的性能肯定是可取的，但它非常不切实际。最好的选择是单独评估您的模型，然后使用例如 2 或 3 个最佳模型创建更简单的组合
  • 例如，考虑您实际上可以拥有加权组合 - 例如0.75 * ets + 0.25 * arima。现在的可能性实际上是无穷无尽的，所以你开始看到蛮力方法的局限性（注意，我认为fable实际上还不支持这些组合）。

也就是说，这是一种可以用来生成所有可能组合的方法。请注意，这可能需要很长时间才能运行 - 但应该会给你你所追求的。

# Get a table of models to get combinations from
fit <- train %>%
  model(
    ets = ETS(Count),
    arima = ARIMA(Count),
    snaive = SNAIVE(Count),
    croston= CROSTON(Count),
    ave=MEAN(Count),
    naive=NAIVE(Count),
    neural=NNETAR(Count),
    lm=TSLM(Count ~ trend()+season())
  )

# Start with a vector containing all the models we want to combine
models <- c("ets", "arima", "snaive", "croston", "ave", "naive", "neural", "lm")

# Generate a table of combinations - if a value is 1, that indicates that
# the model should be included in the combinations
combinations <- models %>% 
  purrr::set_names() %>% 
  map(~0:1) %>% 
  tidyr::crossing(!!!.)

combinations
#> # A tibble: 256 x 8
#>      ets arima snaive croston   ave naive neural    lm
#>    <int> <int>  <int>   <int> <int> <int>  <int> <int>
#>  1     0     0      0       0     0     0      0     0
#>  2     0     0      0       0     0     0      0     1
#>  3     0     0      0       0     0     0      1     0
#>  4     0     0      0       0     0     0      1     1
#>  5     0     0      0       0     0     1      0     0
#>  6     0     0      0       0     0     1      0     1
#>  7     0     0      0       0     0     1      1     0
#>  8     0     0      0       0     0     1      1     1
#>  9     0     0      0       0     1     0      0     0
#> 10     0     0      0       0     1     0      0     1
#> # ... with 246 more rows

# This just filters for combinations with at least 2 models
relevant_combinations <- combinations %>% 
  filter(rowSums(across()) > 1)

# We can use this table to generate the code we would put in a call to `mutate()`
# to generate the combination. {fable} does something funny with code
# evaluation here, meaning that more elegant approaches are more trouble 
# than they're worth
specs <- relevant_combinations %>% 
  mutate(id = row_number()) %>% 
  pivot_longer(-id, names_to = "model", values_to = "flag_present") %>% 
  filter(flag_present == 1) %>% 
  group_by(id) %>% 
  summarise(
    desc = glue::glue_collapse(model, "_"),
    model = glue::glue(
      "({model_sums}) / {n_models}",
      model_sums = glue::glue_collapse(model, " + "),
      n_models = n()
    )
  ) %>% 
  select(-id) %>% 
  pivot_wider(names_from = desc, values_from = model)

# This is what the `specs` table looks like:
specs
#> # A tibble: 1 x 247
#>   neural_lm         naive_lm  naive_neural  naive_neural_lm   ave_lm  ave_neural
#>   <glue>            <glue>    <glue>        <glue>            <glue>  <glue>    
#> 1 (neural + lm) / 2 (naive +~ (naive + neu~ (naive + neural ~ (ave +~ (ave + ne~
#> # ... with 241 more variables: ave_neural_lm <glue>, ave_naive <glue>,
#> #   ave_naive_lm <glue>, ave_naive_neural <glue>, ave_naive_neural_lm <glue>,
#> #   croston_lm <glue>, croston_neural <glue>, croston_neural_lm <glue>,
#> #   croston_naive <glue>, croston_naive_lm <glue>, croston_naive_neural <glue>,
#> #   croston_naive_neural_lm <glue>, croston_ave <glue>, croston_ave_lm <glue>,
#> #   croston_ave_neural <glue>, croston_ave_neural_lm <glue>,
#> #   croston_ave_naive <glue>, croston_ave_naive_lm <glue>, ...

# We can combine our two tables and evaluate the generated code to produce 
# combination models as follows:
combinations <- fit %>% 
  bind_cols(rename_with(specs, ~paste0("spec_", .))) %>% 
  mutate(across(starts_with("spec"), ~eval(parse(text = .))))

# Compute the accuracy for 2 random combinations to demonstrate:
combinations %>% 
  select(sample(seq_len(ncol(.)), 2)) %>% 
  forecast(h = 7) %>% 
  accuracy(count_data, measures = list(
    point_accuracy_measures,
    interval_accuracy_measures,
    distribution_accuracy_measures
  ))
#> # A tibble: 2 x 13
#>   .model          .type    ME  RMSE   MAE   MPE  MAPE  MASE RMSSE   ACF1 winkler
#>   <chr>           <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>
#> 1 spec_ets_arima~ Test  -209. 1014.  771. -40.1  54.0 0.973 0.998 -0.327  30825.
#> 2 spec_ets_snaiv~ Test  -145.  983.  726. -34.5  48.9 0.917 0.967 -0.316  29052.
#> # ... with 2 more variables: percentile <dbl>, CRPS <dbl>
于 2021-12-06T10:14:29.783 回答