Name
Abstract | ||
|---|---|---|
We introduce a novel non-linear forecasting technique based on the Gamma classifier.Its performance for long-term time horizons was tested on synthetic and real data.Two benchmark time series were used for testing.Six time series related to monthly oil production were also used.The Gamma classifier model outperformed previous techniques in forecast accuracy. The paper describes a novel associative model for time series data mining. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, which are both supervised pattern recognition models. The objective is to mine known patterns in the time series in order to forecast unknown values, with the distinctive characteristic that said unknown values may be towards the future or the past of known samples. The proposed model performance is tested both on time series forecasting benchmarks and a data set of oil monthly production. Some features of interest in the experimental data sets are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifier-based predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.patrec.2013.11.008 | Pattern Recognition Letters |
Keywords | Field | DocType |
novel associative model,competitive performance,proposed model performance,time series data mining,unknown value,experimental data set,time series forecasting benchmarks,time series,alpha-beta associative memory,supervised pattern recognition model | Time series,Data mining,Classification of discontinuities,Time series data mining,Associative property,Experimental data,Pattern recognition,Oil production,Artificial intelligence,Classifier (linguistics),Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
41 | C | 0167-8655 |
Citations | PageRank | References |
8 | 0.49 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Itzamá López-Yáñez | 1 | 78 | 11.76 |
| Leonid Sheremetov | 2 | 198 | 28.37 |
| Cornelio Yáñez-Márquez | 3 | 153 | 26.34 |