GDL

Journal from gdlhub / 2017-08-16 08:46:48
Oleh : Minakhi Rout , Babita Majhi , Ritanjali Majhi , Ganapati Panda, King Saud University
Dibuat : 2014-01-16, dengan 1 file

Keyword : Exchange rate forecasting Adaptive auto regressive moving average (ARMA) model Forward backward LMS Particle swarm optimization (PSO) Differential evolution (DE) Cat swarm optimization (CSO) and bacterial foraging optimization (BFO)
Url : http://www.sciencedirect.com/science/article/pii/S1319157813000037
Sumber pengambilan dokumen : web

To alleviate the limitations of statistical based methods of forecasting of exchange rates, soft and evolutionary computing based techniques have been introduced in the literature. To further the research in this direction this paper proposes a simple but promising hybrid prediction model by suitably combining an adaptive autoregressive moving average (ARMA) architecture and differential evolution (DE) based training of its feed-forward and feed-back parameters. Simple statistical features are extracted for each exchange rate using a sliding window of past data and are employed as input to the prediction model for training its internal coefficients using DE optimization strategy. The prediction efficiency is validated using past exchange rates not used for training purpose. Simulation results using real life data are presented for three different exchange rates for one–fifteen months’ ahead predictions. The results of the developed model are compared with other four competitive methods such as ARMA-particle swarm optimization (PSO), ARMA-cat swarm optimization (CSO), ARMA-bacterial foraging optimization (BFO) and ARMA-forward backward least mean square (FBLMS). The derivative based ARMA-FBLMS forecasting model exhibits worst prediction performance of the exchange rates. Comparisons of different performance measures including the training time of the all three evolutionary computing based models demonstrate that the proposed ARMA-DE exchange rate prediction model possesses superior short and long range prediction potentiality compared to others.

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