Financial time series forecasting with machine learning techniques: a survey

By Bjoern Krollner, B. Vanstone, G. Finnie. 2010

193 citations recorded by the source corpus.

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In Time-Series & Forecasting

Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “A number of surveys were published for stock market forecasting studies based on various soft computing methods at different times [24, 25, 26, 27, 28, 29, 30, 31].”
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, background): “Krollner et al.[18] surveyed stock market forecasting papers that use artiﬁcial neural networks, evolutionary optimization techniques, and hybrid methods.”
Indian Stock Market Prediction Using Differential Evolutionary Neural Network Model (2012, background): “Financial time series[1][2][3][4] is highly noisy, irregular, random, non-linear, non-seasonal and chaotic in nature.”
Predicting Stock Price Direction using Support Vector Machines (2015, background): “Patel and Yalamalle agree that ANNs can predict with accuracy slightly greater than 50%, but caution that since stock market data varies so greatly with time and nonlinearly, prediction is difficult even with advanced techniques like ANNs [12].”
A LSTM-based method for stock returns prediction: A case study of China stock market (2015, background): “Prediction of stock market has attracted attention from industry to academia [1, 2].”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “The most popular tool for ﬁnancial prediction seems to be ANN (Krollner et al., 2010).”
Unsupervised Learning Trojan (2014, background): “Among the applications of machine learning are financial applications (Krollner et al., 2010; McNelis, 2005), medical applications (Kononenko, 2001), and intrusion detection (Tsai et al., 2009; Sabhnani & Serpen, 2003a), among many others.”
A deep learning based stock trading model with 2-D CNN trend detection (2017, background): “In recent decades, computational intelligence models that can learn nonlinear relationships between input and output values have shown promise and researchers have been trying to incorporate these models in financial forecasting [1], [2].”
A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021, background): “In [40], the latest literature reviews the techniques used to predict stock market trends in the area of artificial intelligence and machine learning.”