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Found 14 papers, 3 papers with code
A Learnheuristic Approach to A Constrained Multi-Objective Portfolio Optimisation Problem
The results of this study show that, despite taking significantly longer to run to completion, the learnheuristic algorithms outperform the baseline algorithms in terms of hypervolume and rate of convergence.
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting
However, a unified taxonomy for model fusion and an empirical comparison of these hybrid and feature-based stacking ensemble approaches is still missing.
Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets
Our study's contribution is a methodology for tackling missing data problems using a combination of synthetic dataset generation, missing data imputation and deep learning methods to resolve missing data challenges.
Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem
The results also highlight the need to tune the hyper-parameters used by the surrogate-assisted framework, as the surrogate, in some instances, shows some deterioration over the baseline algorithm.
Multi-Modal Recommendation System with Auxiliary Information
Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour.
Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations
Portfolio management is a multi-period multi-objective optimisation problem subject to a wide range of constraints.
Knowledge Graph Fusion for Language Model Fine-tuning
Language Models such as BERT have grown in popularity due to their ability to be pre-trained and perform robustly on a wide range of Natural Language Processing tasks.
Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system
We further find that combining a Genetic Algorithm framework with Machine Learning Surrogate models as a substitute for long-running simulation models yields significant computational efficiency improvements, 1. 7 - 1. 84 times speedup for the increased complexity examples and a 2. 7 times speedup for the Pressure Swing Adsorption system.
Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization
This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem.
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
Difficulties with applying hybrid forecast methods to multivariate data include ($i$) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, ($ii$) challenges associated with auto-correlation inherent in the data, as well as ($iii$) complex dependency (cross-correlation) between the covariates that may be hard to capture.
Multivariate Time Series Forecasting
Prediction Intervals
+1
Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning
We benchmark our approach against the oft-preferred well-established statistical models.
Incremental Class Learning using Variational Autoencoders with Similarity Learning
We show that one does not require stored images (exemplars) for incremental learning with similarity learning.
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves
We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series similar to those in the early days of the COVID-19 pandemic.
Deep Similarity Learning for Sports Team Ranking
Six models were developed and compared, a LightGBM, a XGBoost, a LightGBM (Contrastive Loss), LightGBM (Triplet Loss), a XGBoost (Contrastive Loss), XGBoost (Triplet Loss).
