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This paper is submited for publication on IEEE Transcations for Neural Networks and Learning System (ID: TNNLS-2016-P-6504) This paper presents a novel online adaptive neuro-computing framework for a robust time-series forecast. The proposed framework does mimic the human mind's biological two-thinking model. Our mind makes decisions/calculations using a two-connected system. A first system, System 1, the so-called the intuitive system, makes decisions based on our experience. A second system, System 2, the controller system, does control the decisions of System 1 by either modifying or trusting them. Similarly to the human mind's two-systems model, this paper proposes an artificial framework consisting of two cellular neural network (CNN) systems. The first CNN processor does represent the intuitive system and we call it Intuitive-CNN. The Second CNN processor does represent the controller system, which is called Controller-CNN. Both are connected within a general framework that we name OSA-CNN. The proposed framework is extensively tested, validated and benchmarked with the best state-of-the-art related methods, while involving real field time-series data. Multiple scenarios are considered: traffic flow data extracted from the PeMs traffic database and the 111 time-series collected from the so-called NN3 competitions. The novel OSA-CNN concept does remarkably highly outperform the state-of-the-art competing methods regarding both performance and universality. Index Terms—Time-series forecast (TFS), Cellular neural networks (CNN), Echo state network (ESN), Model reference neural network adaptive control (MRNNAC), Recursive particle swarm optimization (RPSO), Online self-adaptive CNN (OSA-CNN)
A novel approach using modular neural networks to forecast exchange rates based on harmonic patterns in Forex market is introduced. The proposed approach employs three algorithms to predict price, validate its prediction and update the system. The model is trained by historical data using major currencies in Forex market. The proposed system's predictions were evaluated by comparing its results with a non-modular neural network. Results showed that the infrastructure market data consist of significant accurate relations that a single network cannot detect these relations and separate trained networks in specific tasks are needed. Comparison of modular and non-modular systems showed that modular neural network outperforms the other one.
The paper tackles with local models (LM) for periodical time series (TS) prediction. A novel prediction method is introduced, which achieves high prediction accuracy by extracting relevant data from historical TS for LMs training. According to the proposed method, the period of TS is determined by using autocorrelation function and moving average filter. A segment of relevant historical data is determined for each time step of the TS period. The data for LMs training are selected on the basis of the k-nearest neighbours approach with a new hybrid usefulness-related distance. The proposed definition of hybrid distance takes into account usefulness of data for making predictions at a given time step. During the training procedure, only the most informative lags are taken into account. The number of most informative lags is determined in accordance with the Kraskov's mutual information criteria. The proposed approach enables effective applications of various machine learning (ML) techniques for prediction making in expert and intelligent systems. Effectiveness of this approach was experimentally verified for three popular ML methods: neural network, support vector machine, and adaptive neuro-fuzzy inference system. The complexity of LMs was reduced by TS preprocessing and informative lags selection. Experiments on synthetic and real-world datasets, covering various application areas, confirm that the proposed period aware method can give better prediction accuracy than state-of-the-art global models and LMs. Moreover, the data selection reduces the size of training dataset. Hence, the LMs can be trained in a shorter time.
Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models. The paper proposes a hybrid approach integrating the advantages of both decomposition model (namely, Maximal Overlap Discrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR) to predict the National Stock Exchange Fifty Index. In first phase, the data is decomposed into a smaller number of subseries using MODWT. In next phase, each subseries is predicted using machine learning models (i.e., ANN and SVR). The predicted subseries are aggregated to obtain the final forecasts. In final stage, the effectiveness of the proposed approach is evaluated using error measures and statistical test. The proposed methods (MODWT-ANN and MODWT-SVR) are compared with ANN and SVR models and, it was observed that the return on investment obtained based on trading rules using predicted values of MODWT-SVR model was higher than that of Buy-and-hold strategy.