Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified Differential Harmony Search Technique

作者：

Highlights：

• A new neuro-fuzzy network for financial time series prediction is presented.

• A modified Differential Harmony Search algorithm is used for weight updating.

• Local as well as delayed output feedback are used for more accurate forecast.

• Superior predictive ability test is also used for the proposed SERNFIS model.

摘要

•A new neuro-fuzzy network for financial time series prediction is presented.•A modified Differential Harmony Search algorithm is used for weight updating.•Local as well as delayed output feedback are used for more accurate forecast.•Superior predictive ability test is also used for the proposed SERNFIS model.

论文关键词：Recurrent network,Functional Link Artificial Neural Network (FLANN),Artificial Nero Fuzzy Inference System (ANFIS),Harmony search (HS),Differential Evolution (DE),Differential Harmony Search (DHS)

论文评审过程：Received 29 September 2014, Revised 9 January 2016, Accepted 10 January 2016, Available online 20 January 2016, Version of Record 17 February 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.01.016