new feature selection method by a semi supervised classification algorithm for time series estimation

In this research, 12 approaches were proposed to create an optimal vector based on supporting vector machine and neural networks based on genetic algorithm, cuckoo and particle swarm Optimization (PSO). In this regard, we have tried to design a system that reduces the cost of data collection. For this purpose, three data sets with time series capability of standard UCI data were used in this study. The results of the approaches used in this research show the good performance of all the used algorithms. However, the ability and performance of each approach vary according to the type and nature of the data. This has sometimes led to better results from the MLP neural network and the GA or Cuckoo algorithm, and in some cases, the PSO algorithm has better outcomes. Regarding the results, it can be said that the use of feature selection based on semi-regulatory classification reduces system error, increases the accuracy and increases the speed of time series estimation. Hence, by using the efficient and powerful MLP Neural Network and backup vector machine along with the optimization algorithm and metamorphic, an optimal combination classification system can be designed for time series estimation.