# Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data

arXiv: https://arxiv.org/abs/1901.08280 · HTML: https://mlsanity.com/paper/1901.08280/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study](https://mlsanity.com/paper/2308.01915/) (2023)
- [HLOB -- Information Persistence and Structure in Limit Order Books](https://mlsanity.com/paper/2405.18938/) (2024)
- [Deep Limit Order Book Forecasting](https://mlsanity.com/paper/2403.09267/) (2024)
- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://mlsanity.com/paper/2207.11577/) (2022)
- [Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting](https://mlsanity.com/paper/2107.06995/) (2021)
- [The Limit Order Book Recreation Model (LOBRM): An Extended Analysis](https://mlsanity.com/paper/2107.00534/) (2021)
- [Self-Attention Neural Bag-of-Features](https://mlsanity.com/paper/2201.11092/) (2022)
- [Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management](https://mlsanity.com/paper/2309.16679/) (2023)
- [Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading](https://mlsanity.com/paper/2311.02088/) (2023)

## Abstract (arXiv)

Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, velocity and variety of the data collected in these applications pose significant and unique challenges that must be carefully addressed for each of them. In this work, a novel Temporal Logistic Neural Bag-of-Features approach, that can be used to tackle these challenges, is proposed. The proposed method can be effectively combined with deep neural networks, leading to powerful deep learning models for time series analysis. However, combining existing BoF formulations with deep feature extractors pose significant challenges: the distribution of the input features is not stationary, tuning the hyper-parameters of the model can be especially difficult and the normalizations involved in the BoF model can cause significant instabilities during the training process. The proposed method is capable of overcoming these limitations by a employing a novel adaptive scaling mechanism and replacing the classical Gaussian-based density estimation involved in the regular BoF model with a logistic kernel. The effectiveness of the proposed approach is demonstrated using extensive experiments on a large-scale financial time series dataset that consists of more than 4 million limit orders.