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我第一次使用闪亮，我正在尝试制作一个能够接收 CSV 文件、适合一些模型和预测数据（3 个月）的应用程序。

该应用程序提供了一个表格，其中包含拟合模型的准确度测量值，但这些数字与在普通 R 脚本中运行相同模型时的准确度有很大不同。

我想不出这些差异的任何原因！我使用相同的数据文件、模型和变量。同样的训练和测试数据也是如此。模型中的差异要大得多fasster！

任何人都可以看到问题吗？

例如，如果我使用以下模型（代码如下）运行模型函数，则预测 3 个月的准确度测量如下：

R 脚本结果

> erros (forecast: 130.23 sec elapsed)
# A tibble: 5 x 9
  .model         .type    ME    RMSE   MAE   MPE   MAPE   MASE   ACF1
  <chr>          <chr>   <dbl>  <dbl>  <dbl> <dbl>  <dbl> <dbl>  <dbl>
1 ANNC_HOLIDAY   Test   10.2    19.8  14.4   6.41  10.2   NaN   0.131 
2 ARIMA          Test   6.18    19.0  14.2   2.95  10.2   NaN   0.0506
3 ETS_auto       Test   5.46    17.0  12.1   2.78  8.69   NaN   0.135 
4 fasst          Test  -0.0758  13.0  9.82  -1.06  7.45   NaN   0.0695
5 fasst_original Test   7.84    17.9  12.9   4.59  9.16   NaN   0.135

闪亮的APP结果

Accuracy Measures
.model                  .type     ME     RMSE    MAE     MPE     MAPE   MASE   ACF1
 ANNC_HOLIDAY            Test   15.06   29.44   24.77   9.92    18.96   NA    -0.01
  ARIMA                  Test   -1.55   17.85   14.28   -3.18   11.57   NA     0.31
  ETS_auto               Test   -2.34   19.99   15.37   -3.83   12.52   NA     0.11
  fast                   Test   -6.91   49.77   30.47   -8.24   25.00   NA    -0.18
  fasst_original         Test    0.12   90.58   62.47   -4.00   50.84   NA    -0.18

R脚本代码（拟合部分） 

#code for the models (fitmodels: 12.24 sec elapsed)
fit_demand <- train %>%
      model(
     # Seasonal Naive Method (benchmark)
        Benchmark = SNAIVE(Demand, lag = "1 year"),
        Benchmark_week = SNAIVE(Demand, lag = "1 week"),
        ETS_auto = ETS(Demand),
     # ANNS
        ANNC_HOLIDAY = NNETAR(Demand ~ AR(period = "1 day, 1 week") + DaysAfterHoliday, scale_inputs = TRUE),
        ANNC_HOLIDAY_diff = NNETAR(Demand ~ AR(period = "1 day, 1 week") 
                                   + DaysAfterHoliday + Diference, scale_inputs = TRUE),
     #FASSTER
        fasst_original = fasster(Demand ~ WeekDay + fourier(7,3)),
        fasst = fasster(Demand ~ WeekDay + fourier(7,3) + Influenza + DaysAfterHoliday 
                        + Diference + poly(1))
    )

闪亮的APP代码（拟合+预测+准确性）

    # FORECAST ----------------------------------------------------------------
#data_final is the historical data and data_extra_final is the forecasting external data

      # fit models
  fit_demand = reactive({
    train = filter_index(data_final(),.~"2019-09-30")
    train %>% 
      model(
        ETS_auto = ETS(Demand),
        # #ARIMA
        ARIMA = ARIMA(Demand),
        # # ANNS
        ANNC_HOLIDAY = NNETAR(Demand ~ AR(period = "1 day, 1 week"), scale_inputs = TRUE),
        #FASSTER
        fasst_original = fasster(Demand ~ WeekDay + fourier(7,3)),
        fasst = fasster(Demand ~ WeekDay + fourier(7,3) + Influenza + DaysAfterHoliday 
                        + Diference + poly(1))
      )
  })

  # Generate forecasts
  demand_forecast = reactive({
    new_data = filter_index(data_extra_final(),.~"2019-10-01" )
    modelo = fit_demand()
    forecast(modelo, new_data)
  })

  #accuracy
  output$table_erro = renderTable ({
    if(is.null(demand_forecast())){return()}
    test = filter_index(data_final(),.~"2019-10-01" )
    forecasted = demand_forecast()
    as.data.frame(accuracy(forecasted,test))
  })

数据

    dataset = as.tsibble(data.frame(Date = c(2017-05-01, 2017-05-02, 2017-05-03, 2017-05-04, 2017-05-05, 2017-05-06, 2017-05-07, 2017-05-08, 2017-05-09, 2017-05-10, 2017-05-11, 2017-05-12, 2017-05-13, 2017-05-14, 2017-05-15, 2017-05-16, 2017-05-17, 2017-05-18, 2017-05-19, 2017-05-20, 2017-05-21, 2017-05-22, 2017-05-23, 2017-05-24, 2017-05-25, 2017-05-26, 2017-05-27, 2017-05-28, 2017-05-29, 2017-05-30, 2017-05-31),
Demand = c(122, 124, 113, 124, 126, 114, 100, 121, 118, 135, 120, 118, 106, 104, 130, 130, 103, 106, 141, 85, 119, 140, 123, 130, 105, 124, 115, 107, 159, 121, 87),
Weekday=c(1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3)), index = Date)

train = filter_index(data_final(),.~"2017-05-15")
test = filter_index(data_final(),"2017-05-16"~.)
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