Evolving hybrid neural networks with swarm intelligence for forecasting ASEAN inflation
Date of Publication
Master of Science in Economics
School of Economics
Dickson A. Lim
Defense Panel Member
Brian C. Gozun
Macroeconomic policy depends greatly on forecasting. Artificial neural networks (ANNs) such as multilayer perceptron's (MLPs) and recurrent neural networks (RNNs) can learn the nonlinearities of time series, making them strong candidates for improving economic forecasting. We forecast inflation rates from the ASEAN region using the standard automatic SARIMA as benchmark, the MLP, a state of the art RNN called Long Short Term Memory (LSTM), and a novel hybrid SARIMA-ANN model. Neural networks, however, are difficult to design and train. Thus, we let the network hyper parameters evolve using a recent Swarm Intelligence optimization algorithm: Grey Wolf Optimization (2014). We compare the one step and 12-steps ahead forecast accuracy of the evolving ANNs with SARIMA. Results show a clear superiority of the evolving SARIMA-ANN over every other model, with the evolving MLP at second, SARIMA at third, and LSTM performing the worst.
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
1 computer disc ; 4 3/4 in.
Neural networks (Computer science); Economic forecasting; Business forecasting
Cabanilla, K. M. (2018). Evolving hybrid neural networks with swarm intelligence for forecasting ASEAN inflation. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/5515