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Found 20 papers, 5 papers with code
Cryptocurrency Portfolio Optimization by Neural Networks
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation.
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification
The study highlights the potential of subspace learning-based OCC algorithms by investigating the limitations of current fraud detection strategies and the specific challenges of credit card fraud detection.
Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input
In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead.
Optimum Output Long Short-Term Memory Cell for High-Frequency Trading 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.
Early Warning Software for Emergency Department Crowding
We showed that the software could predict next hour crowding with a nominal AUC of 0. 98 and 24 hour crowding with an AUC of 0. 79 using simple statistical models.
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series.
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification
In addition, as the market evolves through time, it is necessary to update the existing models or train new ones when new data is made available.
Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction
Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis.
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network.
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose.
Clusters of investors around Initial Public Offering
The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored.
Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Feature Engineering for Mid-Price Prediction with Deep Learning
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB.
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies.
Deep Adaptive Input Normalization for Time Series Forecasting
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
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.
Using Deep Learning for price prediction by exploiting stationary limit order book features
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems.
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis.
Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis
Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market.
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders.
