--- The dataset contains all time series of the competition (dataset A - the complete dataset of 111 monthly time series drawn from homogeneous population of empirical business time series, and includes 11 time series from dataset B). --- FROM: http://www.neural-forecasting-competition.com/downloads/NN3/datasets/download.htm --- Each TimeSeries is Transposed from original dataset, to lines, each column represent a TimeSeries component, data from "FIT_1" to "FIT_126" should be used to predict "PRED_0" to "PRED_17" THIS DATASET CONTAINS NAN! and they are very important! NaN columns represent a small/bigger timeseries! --- Evaluation: We assume no particular decision problem of the underlying forecasting competition and hence assume symmetric cost of errors. To account for a different number of observations in the individual data sub-samples of training and test set, and the different scale between individual series we propose to use a mean percentage error metric, which is also established best-practice in industry and in previous competitions. All submissions will be evaluated using the mean Symmteric Mean Absolute Percent Error (SMAPE) across al time series. The SMAPE calculates the symmetric absolute error in percent between the actuals X and the forecast F across all observations t of the test set of size n for each time series s with ![EVALUATION FUNCTION][1] (attention: corrected formula from previously published flawed error measure) The SMAPE of each series will then be averaged over all time series in the dataset for a mean SMAPE. To determine a winner, all submissions will be ranked by mean SMAPE across all series. However, biases may be introduced in selecting a “best” method based upon a single metric, particularly in the lack of a true objective or loss function. Therefore, while our primary means of ranking forecasting approaches is mean SMAPE, alternative metrics will be used so as to guarantee the integrity of the presented results. All submitted forecasts will also be evaluated on a number of additional statistical error measures in order to analyse sensitivity to different error metrics. Additional Metrics for reporting purposes include: Average SMAPE (main metric to determine winner) Median SMAPE Median absolute percentage error (MdAPE) Median relative absolute error (MdRAE) Average Ranking based upon the error measures good luck guys :) [1]: http://www.neural-forecasting-competition.com/instructions-Dateien/image002.gif