1 citations · 3 across the 10 of their papers we have counts for
12 papers
Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction
Jonathan Chang, Zimeng Lyu
Accurate forecasting models are usually large, expensive to update online, and fixed in architecture once trained. We apply ONE-NAS, an online neuroevolutionary architecture search…
Forecast Accuracy Is Not Trading Profit: Evolving Small Recurrent Networks for Stock Return Prediction
Jonathan Chang, Zimeng Lyu
Time series forecasting models are typically compared on pointwise error, which scores a prediction in isolation from the decision it is produced for, and a lower forecast error do…
Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions
Devroop Kar, Zimeng Lyu, Sheeraja Rajakrishnan +4
Stock trading has always been a challenging task due to the highly volatile nature of the stock market. Making sound trading decisions to generate profit is particularly difficult…
Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading
Zimeng Lyu, Amulya Saxena, Rohaan Nadeem +2
Stock return forecasting is a major component of numerous finance applications. Predicted stock returns can be incorporated into portfolio trading algorithms to make informed buy o…
Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification
Zimeng Lyu, Pujan Thapa, Travis Desell
Identifying phases of flight is important in the field of general aviation, as knowing which phase of flight data is collected from aircraft flight data recorders can aid in the mo…
Minimally Supervised Learning using Topological Projections in Self-Organizing Maps
Zimeng Lyu, Alexander Ororbia, Rui Li +1
Parameter prediction is essential for many applications, facilitating insightful interpretation and decision-making. However, in many real life domains, such as power systems, medi…
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting
Zimeng Lyu, Alexander Ororbia, Travis Desell
Time series forecasting (TSF) is one of the most important tasks in data science given the fact that accurate time series (TS) predictive models play a major role across a wide var…
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting
Zimeng Lyu, Travis Desell
Time series forecasting (TSF) is one of the most important tasks in data science, as accurate time series (TS) predictions can drive and advance a wide variety of domains including…
Continuous Ant-Based Neural Topology Search
AbdElRahman ElSaid, Joshua Karns, Zimeng Lyu +2
This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), whic…
An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution
Zimeng Lyu, AbdElRahman ElSaid, Joshua Karns +2
Weight initialization is critical in being able to successfully train artificial neural networks (ANNs), and even more so for recurrent neural networks (RNNs) which can easily suff…
Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation
AbdElRahman ElSaid, Joshua Karns, Alexander Ororbia +3
Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset. While this has been shown to be qui…
Improving Neuroevolution Using Island Extinction and Repopulation
Zimeng Lyu, Joshua Karns, AbdElRahman ElSaid +1
Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is through the use of islands, w…