Abstract
The analysis of financial time series for predicting the future developments is a challenging problem since past decades. A forecasting technique based upon the machine learning paradigm and deep learning network namely Extreme Learning Machine with Auto-encoder (ELM-AE) has been proposed. The efficacy and effectiveness of ELM-AE has been compared with few existing forecasting methods like Generalized Autoregressive Conditional Heteroskedastcity (GARCH), General Regression Neural Network (GRNN), Multiple Layer Perceptron (MLP), Random Forest (RF) and Group Method of Data Handling (GRDH). Experimental results have been computed on two different time series data that is Gold Price and Crude Oil Price. The results indicate that the implemented model outperforms existing models in terms of qualitative parameters such as mean square error (MSE).
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References
http://www.quandl.com/data/FRED/DCOILBRENTEU-Crude-Oil-Prices-Brent-Europe
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Preeti, Dagar, A., Bala, R., Singh, R.P. (2018). Financial Time Series Forecasting Using Deep Learning Network. In: Deka, G., Kaiwartya, O., Vashisth, P., Rathee, P. (eds) Applications of Computing and Communication Technologies. ICACCT 2018. Communications in Computer and Information Science, vol 899. Springer, Singapore. https://doi.org/10.1007/978-981-13-2035-4_3
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DOI: https://doi.org/10.1007/978-981-13-2035-4_3
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