Name
Abstract | ||
|---|---|---|
This work describes the first place winner forecasting method for solving the 1st International Competition on Time Series Forecasting (ICTSF 2012). It is based on an already award winning approach of MLP ensembles [1]. The ICTSF 2012 consisted on predicting 8 time series of different time frequency and different forecasting horizons. The main feature of the present method was applying different data pre-processing and seasonality adjustments to a combined forecast of 225 MLPs predicting each time series. Experimental comparison and the competitions result shows that this new predictive system increases its performance in multi-step forecasting when compared to ensembles of MLP. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EAIS.2012.6232819 | Evolving and Adaptive Intelligent Systems |
Keywords | Field | DocType |
data analysis,forecasting theory,multilayer perceptrons,time series,1st International Competition on Time Series Forecasting,ICTSF 2012,MLP ensemble forecasts,MLP ensembles,award winning approach,combined forecast,data preprocessing,data transformations,first place winner forecasting method,forecasting horizons,multistep forecasting,predictive system,seasonality adjustments,time frequency,time series | Econometrics,Technology forecasting,Time series,Consensus forecast,Data transformation (statistics),Computer science,Seasonality,Probabilistic forecasting,Artificial intelligence,Forecasting theory,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-1726-9 | 1 | 0.45 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Domingos S. P. Salazar | 1 | 1 | 0.45 |
| Paulo J. L. Adeodato | 2 | 9 | 1.71 |
| Adrian L. Arnaud | 3 | 21 | 3.67 |