Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes
By Zhixi Li, Vincent W. L. Tam. 2017
30 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Economics and Econometrics, Economics & Operations Research, Signal Processing, Time-Series & Forecasting
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Economics and Econometrics
In Economics & Operations Research
In Signal Processing
In Time-Series & Forecasting
- Weiwei Jiang
- M. Ugur Gudelek
- Omer Berat Sezer
- A. Ozbayoglu
- Ze Hu
- Matloob Khushi
- Yiqi Zhao
- Longbing Cao
Citations in the corpus
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [123, 124, 135, 136, 137] used NIKKEI as their dataset.”
- Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…Sim et al. (2019) CNN ANN, SVM Recurrent Neural Network Type Huynh et al. (2017) BGRU LSTM, GRU, DNN (Ding et al., 2014), DNN (Peng & Jiang, 2016) Li & Tam (2017) WT+LSTM LSTM Li et al. (2017a) LSTM SVM Nelson et al. (2017) LSTM MLP, RF Tsantekidis et al. (2017b) LSTM SVM, MLP Zhang et al.…”
- A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “Skehin, T. proposed a linear Autoregressive Integrated Moving Average (ARIMA) model and LSTM network for each series to produce next-day predictions.”