Big Data Forecasting Using Evolving Multi-layer Perceptron Gold Price Case Study
Rahmat, Romi Fadillah
Alharthi, Adil Fahad
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One of the mostly used commodities in investment is gold. However, gold price tends to have fluctuation. This paper proposed an Evolving Multi-Layer Perceptron (eMLP) to forecast accurately the gold price by considering its daily fluctuate price and utilizing information from a big data of actual dataset. The proposed eMLP algorithm combines the concept of evolving connectionist system and multi-layer perceptron in neural network. This algorithm can expand its own structure based on the incoming input. An experiment was conducted using actual dataset from January 3rd, 2011 to April 26th, 2013 for training purpose and dataset from April 29th, 2013 to April 25th, 2014 for testing. Experiment results showed that the proposed eMLP gives excellent accuracy with the Mean Absolute Percentage Error (MAPE) up to 0.769% for the selected parameters: sensitivity threshold 0.9, error threshold 0.1, learning rate1 0.9, and learning rate2 0.9.