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.
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A data frame with 735 observations on the following 111 variables.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
a numeric vector containing observations of a univariate time series.
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.
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