Description Usage Format Details Source References See Also Examples

The NN5 Competition dataset composed of daily time series originated from the observation of daily withdrawals at 111 randomly selected different cash machines at different locations within England.

1 | ```
data("NN5.A")
``` |

A data frame with 735 observations on the following 111 variables.

`NN5.001`

a numeric vector containing observations of a univariate time series.

`NN5.002`

a numeric vector containing observations of a univariate time series.

`NN5.003`

a numeric vector containing observations of a univariate time series.

`NN5.004`

a numeric vector containing observations of a univariate time series.

`NN5.005`

a numeric vector containing observations of a univariate time series.

`NN5.006`

a numeric vector containing observations of a univariate time series.

`NN5.007`

a numeric vector containing observations of a univariate time series.

`NN5.008`

a numeric vector containing observations of a univariate time series.

`NN5.009`

a numeric vector containing observations of a univariate time series.

`NN5.010`

a numeric vector containing observations of a univariate time series.

`NN5.011`

a numeric vector containing observations of a univariate time series.

`NN5.012`

a numeric vector containing observations of a univariate time series.

`NN5.013`

a numeric vector containing observations of a univariate time series.

`NN5.014`

a numeric vector containing observations of a univariate time series.

`NN5.015`

a numeric vector containing observations of a univariate time series.

`NN5.016`

a numeric vector containing observations of a univariate time series.

`NN5.017`

a numeric vector containing observations of a univariate time series.

`NN5.018`

a numeric vector containing observations of a univariate time series.

`NN5.019`

a numeric vector containing observations of a univariate time series.

`NN5.020`

a numeric vector containing observations of a univariate time series.

`NN5.021`

a numeric vector containing observations of a univariate time series.

`NN5.022`

a numeric vector containing observations of a univariate time series.

`NN5.023`

a numeric vector containing observations of a univariate time series.

`NN5.024`

a numeric vector containing observations of a univariate time series.

`NN5.025`

a numeric vector containing observations of a univariate time series.

`NN5.026`

a numeric vector containing observations of a univariate time series.

`NN5.027`

a numeric vector containing observations of a univariate time series.

`NN5.028`

a numeric vector containing observations of a univariate time series.

`NN5.029`

a numeric vector containing observations of a univariate time series.

`NN5.030`

a numeric vector containing observations of a univariate time series.

`NN5.031`

a numeric vector containing observations of a univariate time series.

`NN5.032`

a numeric vector containing observations of a univariate time series.

`NN5.033`

a numeric vector containing observations of a univariate time series.

`NN5.034`

a numeric vector containing observations of a univariate time series.

`NN5.035`

a numeric vector containing observations of a univariate time series.

`NN5.036`

a numeric vector containing observations of a univariate time series.

`NN5.037`

a numeric vector containing observations of a univariate time series.

`NN5.038`

a numeric vector containing observations of a univariate time series.

`NN5.039`

a numeric vector containing observations of a univariate time series.

`NN5.040`

a numeric vector containing observations of a univariate time series.

`NN5.041`

a numeric vector containing observations of a univariate time series.

`NN5.042`

a numeric vector containing observations of a univariate time series.

`NN5.043`

a numeric vector containing observations of a univariate time series.

`NN5.044`

a numeric vector containing observations of a univariate time series.

`NN5.045`

a numeric vector containing observations of a univariate time series.

`NN5.046`

a numeric vector containing observations of a univariate time series.

`NN5.047`

a numeric vector containing observations of a univariate time series.

`NN5.048`

a numeric vector containing observations of a univariate time series.

`NN5.049`

a numeric vector containing observations of a univariate time series.

`NN5.050`

a numeric vector containing observations of a univariate time series.

`NN5.051`

a numeric vector containing observations of a univariate time series.

`NN5.052`

a numeric vector containing observations of a univariate time series.

`NN5.053`

a numeric vector containing observations of a univariate time series.

`NN5.054`

a numeric vector containing observations of a univariate time series.

`NN5.055`

a numeric vector containing observations of a univariate time series.

`NN5.056`

a numeric vector containing observations of a univariate time series.

`NN5.057`

a numeric vector containing observations of a univariate time series.

`NN5.058`

a numeric vector containing observations of a univariate time series.

`NN5.059`

a numeric vector containing observations of a univariate time series.

`NN5.060`

a numeric vector containing observations of a univariate time series.

`NN5.061`

a numeric vector containing observations of a univariate time series.

`NN5.062`

a numeric vector containing observations of a univariate time series.

`NN5.063`

a numeric vector containing observations of a univariate time series.

`NN5.064`

a numeric vector containing observations of a univariate time series.

`NN5.065`

a numeric vector containing observations of a univariate time series.

`NN5.066`

a numeric vector containing observations of a univariate time series.

`NN5.067`

a numeric vector containing observations of a univariate time series.

`NN5.068`

a numeric vector containing observations of a univariate time series.

`NN5.069`

a numeric vector containing observations of a univariate time series.

`NN5.070`

a numeric vector containing observations of a univariate time series.

`NN5.071`

a numeric vector containing observations of a univariate time series.

`NN5.072`

a numeric vector containing observations of a univariate time series.

`NN5.073`

a numeric vector containing observations of a univariate time series.

`NN5.074`

a numeric vector containing observations of a univariate time series.

`NN5.075`

a numeric vector containing observations of a univariate time series.

`NN5.076`

a numeric vector containing observations of a univariate time series.

`NN5.077`

a numeric vector containing observations of a univariate time series.

`NN5.078`

a numeric vector containing observations of a univariate time series.

`NN5.079`

a numeric vector containing observations of a univariate time series.

`NN5.080`

a numeric vector containing observations of a univariate time series.

`NN5.081`

a numeric vector containing observations of a univariate time series.

`NN5.082`

a numeric vector containing observations of a univariate time series.

`NN5.083`

a numeric vector containing observations of a univariate time series.

`NN5.084`

a numeric vector containing observations of a univariate time series.

`NN5.085`

a numeric vector containing observations of a univariate time series.

`NN5.086`

a numeric vector containing observations of a univariate time series.

`NN5.087`

a numeric vector containing observations of a univariate time series.

`NN5.088`

a numeric vector containing observations of a univariate time series.

`NN5.089`

a numeric vector containing observations of a univariate time series.

`NN5.090`

a numeric vector containing observations of a univariate time series.

`NN5.091`

a numeric vector containing observations of a univariate time series.

`NN5.092`

a numeric vector containing observations of a univariate time series.

`NN5.093`

a numeric vector containing observations of a univariate time series.

`NN5.094`

a numeric vector containing observations of a univariate time series.

`NN5.095`

a numeric vector containing observations of a univariate time series.

`NN5.096`

a numeric vector containing observations of a univariate time series.

`NN5.097`

a numeric vector containing observations of a univariate time series.

`NN5.098`

a numeric vector containing observations of a univariate time series.

`NN5.099`

a numeric vector containing observations of a univariate time series.

`NN5.100`

a numeric vector containing observations of a univariate time series.

`NN5.101`

a numeric vector containing observations of a univariate time series.

`NN5.102`

a numeric vector containing observations of a univariate time series.

`NN5.103`

a numeric vector containing observations of a univariate time series.

`NN5.104`

a numeric vector containing observations of a univariate time series.

`NN5.105`

a numeric vector containing observations of a univariate time series.

`NN5.106`

a numeric vector containing observations of a univariate time series.

`NN5.107`

a numeric vector containing observations of a univariate time series.

`NN5.108`

a numeric vector containing observations of a univariate time series.

`NN5.109`

a numeric vector containing observations of a univariate time series.

`NN5.110`

a numeric vector containing observations of a univariate time series.

`NN5.111`

a numeric vector containing observations of a univariate time series.

The NN5 Competition's Dataset A contains 111 different daily time series. Each of these time series possesses 735 observations, and may present missing data. The time series also show different patterns of single or multiple overlying seasonal properties.
Each competitor in NN5 was asked to predict the next 56 corresponding observations of each times series (`NN5.A.cont`

).
The performance evaluation done by NN5 Competition was based on the mean SMAPE error of prediction found by the competitors across all time series.

NN5 2008, The NN5 Competition: Forecasting competition for artificial neural networks and computational intelligence. URL: http://www.neural-forecasting-competition.com/NN5/index.htm.

S.F. Crone, 2008, Results of the NN5 time series forecasting competition. Hong Kong, Presentation at the IEEE world congress on computational intelligence. WCCI'2008.

1 2 3 |

```
'data.frame': 735 obs. of 111 variables:
$ NN5.001: num 13.4 14.7 20.6 34.7 26.6 ...
$ NN5.002: num 11.6 13.6 15 21.6 19.4 ...
$ NN5.003: num 5.64 14.4 24.42 28.78 20.62 ...
$ NN5.004: num 13.18 8.45 19.52 28.88 19.47 ...
$ NN5.005: num 9.78 10.81 21.61 38.52 24.74 ...
$ NN5.006: num 9.24 11.64 12.1 21.41 24.67 ...
$ NN5.007: num 14.9 16.3 16.7 23.6 26.3 ...
$ NN5.008: num 2.89 12.36 16.38 30.16 31.18 ...
$ NN5.009: num 7.34 9.16 10.59 12.5 7.16 ...
$ NN5.010: num 10.3 12.7 14.4 19.4 21.5 ...
$ NN5.011: num 13.9 13 19 27.5 24.2 ...
$ NN5.012: num 10.1 15.5 14.6 19.2 22 ...
$ NN5.013: num 12.1 11.4 18.5 24.7 30 ...
$ NN5.014: num 11.3 12.7 17.5 23.4 22.6 ...
$ NN5.015: num 8.23 13.11 18.35 25.92 19.63 ...
$ NN5.016: num 8.52 14.5 30.57 45.42 48.74 ...
$ NN5.017: num 15 15.6 17.7 29.4 35.8 ...
$ NN5.018: num 11.6 13.3 27.5 33.8 32.2 ...
$ NN5.019: num 15.9 17 30.7 44.7 30.3 ...
$ NN5.020: num 13.2 12.6 13 21.4 22.1 ...
$ NN5.021: num 13.4 14 14.2 24.2 21.8 ...
$ NN5.022: num 11.93 10.05 20.32 22.7 5.99 ...
$ NN5.023: num 7.36 7.06 12.11 18.66 15.64 ...
$ NN5.024: num 16.45 19.1 32.64 45.76 3.95 ...
$ NN5.025: num 9.47 11.88 17.96 25.07 19.83 ...
$ NN5.026: num 4.51 7.3 6.08 9.09 11.14 ...
$ NN5.027: num 5.65 5.22 5.8 7.94 9.08 ...
$ NN5.028: num 9.11 10.19 17.91 18.85 19.5 ...
$ NN5.029: num 12.4 15.9 24.1 33 29.8 ...
$ NN5.030: num 12 14.4 27.5 45.7 35 ...
$ NN5.031: num 11.4 5.6 14.2 20.9 25 ...
$ NN5.032: num 13.7 14.1 21.7 33.9 28.4 ...
$ NN5.033: num 11.2 12.7 22.9 34.9 26 ...
$ NN5.034: num 17.1 17.1 25.9 43.1 34.9 ...
$ NN5.035: num 10.6 13.9 20.4 29.6 21.9 ...
$ NN5.036: num 5.88 10.09 14.92 16.67 18.49 ...
$ NN5.037: num 16.1 15.46 20.81 23.46 7.89 ...
$ NN5.038: num 14.9 17.8 32.9 35.5 30.7 ...
$ NN5.039: num 12.4 10.6 15.9 21.9 28.6 ...
$ NN5.040: num 10 14.4 18.9 21 22 ...
$ NN5.041: num 11.6 12.1 20.3 29.7 22.6 ...
$ NN5.042: num 11.5 14 19.6 31.2 26.5 ...
$ NN5.043: num 15.5 20.1 25.4 29.5 27.8 ...
$ NN5.044: num 11.6 14.8 19.3 26.2 21.2 ...
$ NN5.045: num 8.6 10.8 11.1 18 22.8 ...
$ NN5.046: num 11.07 9.84 16.55 17.55 18.61 ...
$ NN5.047: num 13.4 14.7 20.6 34.7 26.6 ...
$ NN5.048: num 12.4 12.4 25.3 26 30.6 ...
$ NN5.049: num 10.4 13.2 16.4 25.5 46.1 ...
$ NN5.050: num 13.9 13 19 27.5 24.2 ...
$ NN5.051: num 9.84 7.02 11.6 16.04 18.28 ...
$ NN5.052: num 12 11.5 23.3 38.2 35.7 ...
$ NN5.053: num 6.72 7.69 9.18 12.24 13.23 ...
$ NN5.054: num 12.5 12.4 14.5 20.4 24.9 ...
$ NN5.055: num 4.75 5.96 4.71 9.98 10.78 ...
$ NN5.056: num 7.785 10.692 0.565 19.976 15.479 ...
$ NN5.057: num 11.8 15.9 16.4 19.3 25 ...
$ NN5.058: num 10.5 14.5 21.8 27.3 30 ...
$ NN5.059: num 7.56 11.18 11.14 15.44 22.75 ...
$ NN5.060: num 10.4 13.7 13.5 17.9 22.8 ...
$ NN5.061: num 11 11.3 15.4 22.9 26.8 ...
$ NN5.062: num 11.6 12.5 23.2 26.9 29.8 ...
$ NN5.063: num 8.35 8.23 12.81 16.99 18.74 ...
$ NN5.064: num 10.85 12.41 15.06 22.32 8.86 ...
$ NN5.065: num 14.9 20.6 20.1 36.9 33.2 ...
$ NN5.066: num 14.4 16.9 19.6 31.4 31.8 ...
$ NN5.067: num 22.1 18.4 24.6 30.6 31.1 ...
$ NN5.068: num 26.9 23.8 26.9 33.2 33.9 ...
$ NN5.069: num 10.6 12.6 12.6 18.8 28 ...
$ NN5.070: num 10.81 9.89 15.49 19 24.79 ...
$ NN5.071: num 8.97 9.15 12.54 16.9 12.83 ...
$ NN5.072: num 5.69 12.84 14.16 19.84 16.5 ...
$ NN5.073: num 6.43 6.31 9.68 14.83 21.67 ...
$ NN5.074: num 9.26 7.59 9.15 13.41 16.19 ...
$ NN5.075: num 16.9 12.6 19 28.6 33.5 ...
$ NN5.076: num 9.69 9.26 8.97 12.72 14.08 ...
$ NN5.077: num 11.5 10.7 15.3 16.4 19.9 ...
$ NN5.078: num 11.7 13.9 12.7 18.3 22 ...
$ NN5.079: num 8.34 9.88 16.32 22.55 18.1 ...
$ NN5.080: num 12.9 14.3 19 29.7 26.9 ...
$ NN5.081: num 4.13 4.58 6.56 7.46 9.28 ...
$ NN5.082: num 6.09 6.19 9.32 15.44 12.38 ...
$ NN5.083: num 10.1 12.7 22.3 37.2 31.3 ...
$ NN5.084: num 6.56 7.98 11.53 11.65 13.2 ...
$ NN5.085: num 9.76 7.1 11.8 18.21 21.23 ...
$ NN5.086: num 8.97 10.39 9.34 13.36 20.37 ...
$ NN5.087: num 14.7 17.8 24.1 44.1 33.4 ...
$ NN5.088: num 19.24 21.63 25.57 16.77 9.77 ...
$ NN5.089: num 12.76 11.89 13.6 17.87 9.15 ...
$ NN5.090: num 12.2 13.1 21.1 32.9 26.6 ...
$ NN5.091: num 12.5 10.5 15.7 12.5 0 ...
$ NN5.092: num 10.8 13.9 11 20.8 26.2 ...
$ NN5.093: num 14.3 11.1 13 21 20.2 ...
$ NN5.094: num 16.7 13.4 17.1 22.2 21.3 ...
$ NN5.095: num 20.83 18.2 20.57 5.08 29.06 ...
$ NN5.096: num 15.1 13.5 17.2 20.5 27.6 ...
$ NN5.097: num 9.52 6.4 10.49 11.93 14.08 ...
$ NN5.098: num 13.4 16.2 16.5 24.1 29.1 ...
$ NN5.099: num 8.61 7.6 12.74 13.76 13.62 ...
[list output truncated]
```

TSPred documentation built on June 26, 2018, 5:04 p.m.

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