NN3: NN3 Time Series Competition - Dataset A

In tspredit: Time Series Prediction with Integrated Tuning

NN3 Time Series Competition - Dataset A

Description

Monthly time series from the NN3 forecasting competition. Data Type: Empirical business time series. Category: Benchmark. Observations: 50 to 126 per series, 111 series. The dataset contains 111 univariate monthly time series from real business processes. Each series has between 50 and 126 observations. Participants were asked to forecast the next 18 values, and performance was evaluated using the mean sMAPE across all series.

Usage

data(NN3)

Format

A data frame with up to 126 rows and 111 columns. Each column corresponds to a different univariate monthly time series.

Details

NN3 comprises monthly business time series with varying lengths. Forecast accuracy is typically evaluated using sMAPE across a fixed holdout horizon.

Source

NN3 Time Series Forecasting Competition

References

Crone, S.F., Hibon, M., & Nikolopoulos, K. (2011). Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3), 635–660. NN3 Competition (2007). http://www.neural-forecasting-competition.com/NN3/index.htm

Examples

# Load NN3 dataset
data(NN3)
# NN3 <- loadfulldata(NN3)

# Select one series by name and plot
series <- NN3[["NN3_111"]]
ts.plot(series, ylab = "Value", xlab = "Month", main = "NN3 example series")

tspredit documentation built on Feb. 11, 2026, 9:08 a.m.