NN3 data

Data from the NN3 forecasting competition, comprising 111 monthly time series each of class ts.

Training and test data are combined. In the competition, the last 18 months were used as test data. Time series NN3-101 to NN3-111 made up the reduced data set from the competition.

head(nn3[[1]])
#>       Jan  Feb  Mar  Apr  May  Jun
#> 1990 5520 3940 4490 5030 5660 4790
autoplot(nn3[[1]])

NN5 data

Data from the NN5 forecasting competition, comprising 111 daily time series each of lass msts.

Training and test data are combined. In the competition, the last 56 days were used as test data. Time series NN5-101 to NN5-111 made up the reduced data set from the competition.

head(nn3[[2]])
#>       Jan  Feb  Mar  Apr  May  Jun
#> 1990 5080 3690 4260 3920 4290 3840
autoplot(nn5[[2]])

NNGC1 data

Data from the NNGC1 forecasting competition, comprising 11 annual time series, 11 quarterly time series, 11 monthly time series, 11 weekly time series, 11 daily time series and 11 hourly time series each of class ts or msts. Only training data are provided.

head(nngc1[[3]])
#> Time Series:
#> Start = 1974 
#> End = 1979 
#> Frequency = 1 
#> [1]  40.833  85.797  95.567  99.921 106.035 106.587
autoplot(nngc1[[3]])

GEFCOM2012 load data

Data from the GEFCOM2012 forecasting competition which was hosted on the kaggle platform. The data comprise 20 time series containing hourly load data from 20 zones in the United States, each of class msts. Only training data are provided. The missing data in each series formed the test sets.

head(gefcom2012_load[[4]])
#> Multi-Seasonal Time Series:
#> Start: 2004 1
#> Seasonal Periods: 24 168 8766
#> Data:
#> [1] 484 457 450 448 444 490
autoplot(gefcom2012_load[[4]])

GEFCOM2012 temperature data

Data from the GEFCOM2012 forecasting competition which held on the kaggle platform. The data comprise 11 time series containing hourly temperature data from 11 weather stations in the United States, each of class msts.

head(gefcom2012_temp[[5]])
#> Multi-Seasonal Time Series:
#> Start: 2004 1
#> Seasonal Periods: 24 168 8766
#> Data:
#> [1] 42 42 40 39 40 41
autoplot(gefcom2012_temp[[5]])

GEFCOM2012 wind power data

Data from the GEFCOM2012 forecasting competition which held on the kaggle platform. The data comprise 7 hourly time series containing wind power data from 7 wind farms, each of class msts. Only training data are provided. The missing data in each series formed the test sets.

head(gefcom2012_wp[[6]])
#> Multi-Seasonal Time Series:
#> Start: 2009 4384
#> Seasonal Periods: 24 168 8766
#> Data:
#> [1] 0.118 0.066 0.026 0.013 0.000 0.013
autoplot(gefcom2012_wp[[6]])

Mcomp: M1 data

The 1001 time series from the M competition, taken from demography, industry and economics, and ranging in length between 9 and 132 observations. All the data were either non-seasonal (e.g., annual), quarterly or monthly. All the data were positive, which made it possibly to compute mean absolute percentage errors, but is not really reflective of the population of real data.

M1 is of class Mcomp with each time series of class Mdata. The function subset inherited from the Mcomp package can return a subset specified by periods, or types of data or both. See the Mcomp package for more details.

M1
#> M-Competition data: 1001 time series 
#> 
#>            Type of data
#> Period      DEMOGR INDUST MACRO1 MACRO2 MICRO1 MICRO2 MICRO3 Total
#>   MONTHLY       75    183     64     92     10     89    104   617
#>   QUARTERLY     39     18     45     59      5     21     16   203
#>   YEARLY        30     35     30     29     16     29     12   181
#>   Total        144    236    139    180     31    139    132  1001
autoplot(M1[[7]])

subset(M1,"monthly")
#> M-Competition data: 617 MONTHLY time series
#> 
#>          Type of data
#> Period    DEMOGR INDUST MACRO1 MACRO2 MICRO1 MICRO2 MICRO3
#>   MONTHLY     75    183     64     92     10     89    104

The 111 series used in the extended comparisons in the 1982 M-competition can be selected as follows.

subset(M1,111)
#> M-Competition data: 111 time series 
#> 
#>            Type of data
#> Period      DEMOGR INDUST MACRO1 MACRO2 MICRO1 MICRO2 MICRO3 Total
#>   MONTHLY        8     21      8      9      1     10     11    68
#>   QUARTERLY      5      2      5      6      0      3      2    23
#>   YEARLY         3      4      4      3      2      3      1    20
#>   Total         16     27     17     18      3     16     14   111

The data in the Mcomp and Tcomp packages are in the Mcomp class which contains various information used in the competitions including the training and test portions of the time series. The function combine_training_test combines the training data and test data into a single ts object.

m1ts <- combine_training_test(M1)

Mcomp: M3 data

The time series from the M3 forecasting competition and the forecasts from all the original participating methods are stored in M3 and M3Forecast. M3 is a list of 3003 series of class Mcomp. Each series within M3 is of class Mdata. M3Forecast is a list of data.frames. See the Mcomp package for more details.

M3
#> M-Competition data: 3003 time series 
#> 
#>            Type of data
#> Period      DEMOGRAPHIC FINANCE INDUSTRY MACRO MICRO OTHER Total
#>   MONTHLY           111     145      334   312   474    52  1428
#>   OTHER               0      29        0     0     4   141   174
#>   QUARTERLY          57      76       83   336   204     0   756
#>   YEARLY            245      58      102    83   146    11   645
#>   Total             413     308      519   731   828   204  3003
autoplot(M3[[8]])

subset(M3, "macro")
#> M-Competition data: 731 MACRO time series
#> 
#>            Type of data
#> Period      MACRO
#>   MONTHLY     312
#>   QUARTERLY   336
#>   YEARLY       83

Tcomp: tourism forecasting competition data

Data from the tourism forecasting competition described in Athanasopoulos, Hyndman, Song and Wu (2011).

tourism is a list of 1,311 series of class Mcomp, and each individual series is an element of class Mdata. See the Tcomp package for more details.

tourism
#> M-Competition data: 1311 TOURISM time series
#> 
#>            Type of data
#> Period      TOURISM
#>   MONTHLY       366
#>   QUARTERLY     427
#>   YEARLY        518
autoplot(tourism[[9]])