Predicting stock market index using fusion of machine learning techniques

By Jigar Patel, Sahil R. Shah, Priyank Thakkar, Ketan Kotecha. 2015

567 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Signal Processing

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In Signal Processing

Citations in the corpus

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “With the developement of deep learning, more research efforts have been paid on exploiting deep neurual networks for financial prediction [2, 8, 12, 14, 21, 25].”
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, methodology): “While there are many approaches to stock price prediction [2, 18, 23, 30], none of existing models, to our best knowledge, explicitly decompose trading patterns into various frequency components, and seamlessly integrate the discovered multi-frequency patterns into price predictions.”
Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, unknown): “It has been used in different fields like in banking for the prediction of response of customer [68], prices direction in stock exchange [69], in pharmaceutical and medicine production [70], and so on.”
Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques (2019, methodology): “[16] have proposed a hybrid model of two-stage approach based on machine learning methods to estimate the future values of the stock market index.”
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting (2020, background): “Time-series forecasting is an emerging topic in machine learning, which can be divided into two major categories: univariate techniques [20, 22, 18, 27, 32, 19, 18] and multivariate techniques [24, 21, 17, 31, 3, 29, 25, 16, 15].”
Stock prediction using deep learning (2016, methodology): “Fusion technique was used in Indian context to model stock data [22].”
EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction (2019, methodology): “, 2015) and hybrid methods (Armano et al., 2005; Wang et al., 2011; Patel et al., 2015), have been widely employed in forecasting stock prices.”
Applications of deep learning in stock market prediction: recent progress (2020, background): “…on next page Table 10 – continued from previous page Metrics Article List Mean Absolute Percentage Error (MAPE) de Oliveira et al. (2013); Ticknor (2013); Patel et al. (2015); Bao et al. (2017); Li & Tam (2017); Qin et al. (2017); Singh & Srivastava (2017); Yang et al. (2017); Baek & Kim…”