TY - JOUR
T1 - A novel associative model for time series data mining
AU - López-Yáñez, Itzamá
AU - Sheremetov, Leonid
AU - Yáñez-Márquez, Cornelio
N1 - Funding Information:
This research was partially supported by the CONACYT-SENER (project 146515 ), as well as the Instituto Politécnico Nacional (Secretaría Académica, COFAA, SIP, CIDETEC and CIC), CONACyT, and SNI.
PY - 2014/5/1
Y1 - 2014/5/1
N2 - 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.
AB - 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.
KW - Associative models
KW - CATS benchmark
KW - Mackey-Glass benchmark
KW - Oil production time series
KW - Supervised classification
KW - Time series data mining
UR - http://www.scopus.com/inward/record.url?scp=84897591518&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2013.11.008
DO - 10.1016/j.patrec.2013.11.008
M3 - Artículo
SN - 0167-8655
VL - 41
SP - 23
EP - 33
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 1
ER -