Enhanced prediction for investment portfolio management using multiple kernel-based long short-term memory-network for nickel price prediction

Nickel is a key competitive material source with commodities and economic characteristics, the price volatility of which would influence shareholder's decisions. For this reason, an accurate pattern prediction of nickel prices is a crucial concern for the strategic planning of investors in the nickel trade; however, the conventional neural network models are not effective in terms of predictive performance and suitability. So, Gaussian Processes-Long Short-Term Memory (GP-LSTM) model was suggested to learn the LSTM kernels and predict the nickel prices. However, this kernel function representation was not plenty for a huge amount of data captured from various sources. Also, it cannot completely learn dynamic, composite and structural data attributes. Therefore in this article, a Multiple-Kernel LSTM (MKLSTM) network model is proposed for learning the huge amount of data and predicting the nickel prices efficiently. The core objective of this MKLSTM model is to resolve the difficulty of adding LSTM and multiple kernel functions using data from nickel industries. This model is applied to find the significant attributes for representing suitable data. Also, the proper larger kernels for learning dynamic and structural data attributes are selected by constructing a Directed Acyclic Graph (DAG). By using the selected kernels, the MKLSTM is learned to predict the nickel price and its fluctuations. At last, the experimental results exhibit the performance of MKLSTM using the nickel price database compared to the LSTM and GP-LSTM models.