Time-series forecasting using GA-tuned radial basis functions
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
System Identification using Structured Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Forecasting time series with a new architecture for polynomial artificial neural network
Applied Soft Computing
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
Probabilistic incremental program evolution
Evolutionary Computation
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
Dynamics of genetic programming and chaotic time series prediction
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Quarterly Time-Series Forecasting With Neural Networks
IEEE Transactions on Neural Networks
Two-fold spatiotemporal regression modeling in wireless sensor networks
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Monthly streamflow forecasting based on improved support vector machine model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Forecasting model selection through out-of-sample rolling horizon weighted errors
Expert Systems with Applications: An International Journal
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hi-index | 12.06 |
Forecasting has always been a crucial challenge for organizations as they play an important role in making many critical decisions. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. In these models most researchers assumed linear relationship among the past values of the forecast variable. Although the linear assumption makes it easier to manipulate the models mathematically, it can lead to inappropriate representation of many real-world patterns in which non-linear relationship is prevalent. This paper introduces a new time-series forecasting model based on non linear regression which has high flexibility to fit any number of data without pre-assumptions about real patterns of data and its fitness function. To estimate the model parameters, we have used hybrid metaheuristic which has the ability of estimating the optimal value of model parameters. The proposed hybrid approach is simply structured, and comprises two components: a particle swarm optimization (PSO) and a simulated annealing (SA). The hybridization of a PSO with SA, combining the advantages of these two individual components, is the key innovative aspect of the approach. The performance of the proposed method is evaluated using standard test problems and compared with those of related methods in literature, ARIMA and SARIMA models. The results in solving on 11 problems with different structure reveal that the proposed model yields lower errors for these data sets.