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Found 18 papers, 3 papers with code
Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model
We present and release PHS-BERT, a transformer-based PLM, to identify tasks related to public health surveillance on social media.
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions
Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making.
Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT
We present BioALBERT, a domain-specific adaptation of A Lite Bidirectional Encoder Representations from Transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine tuned for 6 different tasks across 20 benchmark datasets.
Feature importance recap and stacking models for forex price prediction
Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade.
Clustering and attention model based for intelligent trading
The foreign exchange market has taken an important role in the global financial market.
Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU
In this study, to classify vaccine sentiment tweets with limited information, we present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention.
Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
To reject the Efficient Market Hypothesis a set of 5 technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market.
A Survey of Forex and Stock Price Prediction Using Deep Learning
Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting.
Text Mining of Stocktwits Data for Predicting Stock Prices
These datasets were labelled with three labelling techniques based on stock price changes.
SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features
SMOTE-NC can be applied only on mixed datasets that have features consisting of both continuous and nominal features and cannot function if all the features of the dataset are nominal.
Wavelet Denoised-ResNet CNN and LightGBM Method to Predict Forex Rate of Change
In this paper, we propose a Wavelet Denoised-ResNet with LightGBM model to predict the rate of change of Forex price after five time intervals to allow enough time to execute trades.
Event-Driven LSTM For Forex Price Prediction
The majority of studies in the field of AI guided financial trading focus on purely applying machine learning algorithms to continuous historical price and technical analysis data.
Portfolio Optimization with 2D Relative-Attentional Gated Transformer
In our research, a conservative level of transaction fees and slippage are considered for the realistic experiment.
BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition
In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially.
Open source tools for management and archiving of digital microscopy data to allow integration with patient pathology and treatment information
These tools also enable large files to be sectioned for image analysis.
Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price
Every change of trend in the forex market presents a great opportunity as well as a risk for investors.
GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading
The parameters of technical indicators are optimized to maximize trading performance.
Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery Prediction
Existing techniques from Information Retrieval and Natural Language Processing attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these techniques undermine the concept of predicting the future and emerging relations among scientific knowledge components encapsulated within the literature.
