# Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting

arXiv: https://arxiv.org/abs/2304.09840 · HTML: https://mlsanity.com/paper/2304.09840/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 64 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 25 of 25)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [A Simple Way to Initialize Recurrent Networks of Rectified Linear Units](https://mlsanity.com/paper/1504.00941/) (2015)
- [Sequential Neural Models with Stochastic Layers](https://mlsanity.com/paper/1605.07571/) (2016)
- [Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences](https://mlsanity.com/paper/1610.09513/) (2016)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Can recurrent neural networks warp time?](https://mlsanity.com/paper/1804.11188/) (2018)
- [Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations](https://mlsanity.com/paper/1606.01305/) (2016)
- [Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods](https://mlsanity.com/paper/1705.03233/) (2017)
- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://mlsanity.com/paper/1712.00975/) (2017)
- [Semantic Object Parsing with Graph LSTM](https://mlsanity.com/paper/1603.07063/) (2016)
- [A Siamese Long Short-Term Memory Architecture for Human Re-Identification](https://mlsanity.com/paper/1607.08381/) (2016)
- [Using Deep Learning for price prediction by exploiting stationary limit order book features](https://mlsanity.com/paper/1810.09965/) (2018)
- [Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2010.13891/) (2020)
- [Architectural Complexity Measures of Recurrent Neural Networks](https://mlsanity.com/paper/1602.08210/) (2016)
- [A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition](https://mlsanity.com/paper/1606.06871/) (2016)
- [Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay](https://mlsanity.com/paper/2105.05944/) (2021)
- [Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks](https://mlsanity.com/paper/1701.03441/) (2017)
- [Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning](https://mlsanity.com/paper/1711.01577/) (2017)
- [Feature Engineering for Mid-Price Prediction with Deep Learning](https://mlsanity.com/paper/1904.05384/) (2019)
- [Learning Simpler Language Models with the Differential State Framework](https://mlsanity.com/paper/1703.08864/) (2017)
- [On the Initialization of Long Short-Term Memory Networks](https://mlsanity.com/paper/1912.10454/) (2019)
- [Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning](https://mlsanity.com/paper/2006.04667/) (2020)

## Built on by (most cited first, 2 of 2)

- [Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks](https://mlsanity.com/paper/2412.16160/) (2024)
- [Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading](https://mlsanity.com/paper/2412.19372/) (2024)

## Abstract (arXiv)

High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in high-frequency trading. A well-documented and tested method that considers these time-irregularities is a type of recurrent neural network, named long short-term memory neural network. This type of neural network is formed based on cells that perform sequential and stale calculations via gates and states without knowing whether their order, within the cell, is optimal. In this paper, we propose a revised and real-time adjusted long short-term memory cell that selects the best gate or state as its final output. Our cell is running under a shallow topology, has a minimal look-back period, and is trained online. This revised cell achieves lower forecasting error compared to other recurrent neural networks for online high-frequency trading forecasting tasks such as the limit order book mid-price prediction as it has been tested on two high-liquid US and two less-liquid Nordic stocks.