A new tree-based ensemble model that is called tree-based moving average (TBMA) is provided for time series forecasting problems. The TBMA model provides point and probabilistic forecasts and uses both of the tree-based ensemble and MA approaches to consider predictors and time series components. The suggested model can handle a large number of numerical and categorical predictors without sacrificing the accuracy with the help of the tree-based ensemble approach. The integration of the MA model to a tree-based ensemble approach helps the TBMA model to capture autocorrelation between observations. With the use of the tree-based ensemble and MA approaches, the proposed model becomes multivariate and adapted to time series problems.
You can install the released version of tbma from GitHub with:
This is a basic example which shows you how to solve a common problem:
library(datasets)
library(data.table)
library(tbma)
data(airquality)
summary(airquality)
#> Ozone Solar.R Wind Temp
#> Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
#> 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
#> Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
#> Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
#> 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
#> Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
#> NA's :37 NA's :7
#> Month Day
#> Min. :5.000 Min. : 1.0
#> 1st Qu.:6.000 1st Qu.: 8.0
#> Median :7.000 Median :16.0
#> Mean :6.993 Mean :15.8
#> 3rd Qu.:8.000 3rd Qu.:23.0
#> Max. :9.000 Max. :31.0
#>
airquality<-as.data.table(airquality)
airquality[complete.cases(airquality)]
#> Ozone Solar.R Wind Temp Month Day
#> 1: 41 190 7.4 67 5 1
#> 2: 36 118 8.0 72 5 2
#> 3: 12 149 12.6 74 5 3
#> 4: 18 313 11.5 62 5 4
#> 5: 23 299 8.6 65 5 7
#> ---
#> 107: 14 20 16.6 63 9 25
#> 108: 30 193 6.9 70 9 26
#> 109: 14 191 14.3 75 9 28
#> 110: 18 131 8.0 76 9 29
#> 111: 20 223 11.5 68 9 30
train <- airquality[1:102,]
test <- airquality[103:nrow(airquality), ]
output<-tbma(Temp ~ .,train = train,test = test,
prediction_type = "point",horizon=100,ma_order = 2)
library(datasets)
#this is the input data
head(airquality,5)
#> Ozone Solar.R Wind Temp Month Day
#> 1: 41 190 7.4 67 5 1
#> 2: 36 118 8.0 72 5 2
#> 3: 12 149 12.6 74 5 3
#> 4: 18 313 11.5 62 5 4
#> 5: NA NA 14.3 56 5 5
#this is the output data (Order and prediction columns are added by the tbma() function)
head(output,5)
#> Order Temp Ozone Solar.R Wind Month Day prediction
#> 1: 66 86 44 192 11.5 8 12 83.5
#> 2: 67 82 28 273 11.5 8 13 81.5
#> 3: 68 80 65 157 9.7 8 14 85.0
#> 4: 69 77 22 71 10.3 8 16 81.5
#> 5: 70 79 59 51 6.3 8 17 83.5``