Study finds common poetic themes across languages over time.
problem Understanding thematic evolution in different poetic traditions.
method Applied Latent Dirichlet Allocation (LDA) to poetry corpora of four languages.
result Identified common themes and their temporal trends across poetic traditions.
Music genre classification is a widely researched topic in music information retrieval (MIR). Being able to automatically tag genres will benefit music streaming service providers such as JOOX, Apple Music, and Spotify for their content-based recommendation. However, most studies on music classification have been done …
Fibonacci Ensembles use Fibonacci weights to improve ensemble learning, inspired by natural growth patterns.
problem Improving ensemble learning methods to enhance model performance and interpretability.
method Introduces Fibonacci weights and a recursive ensemble dynamic to reduce variance and enrich representational depth.
result Fibonacci weighting can match or improve upon uniform averaging in ensemble learning experiments.
Traditional methods outperform LLMs in forecasting corporate credit ratings.
problem Forecasting corporate credit ratings using LLMs.
method Comparison of traditional methods (XGBoost) and LLMs (LLaMA) on credit rating forecasting.
result XGBoost outperforms LLMs in forecasting corporate credit ratings.
Deep learning outperforms traditional methods in estimating OU process parameters.
problem Parameter estimation of the Ornstein-Uhlenbeck process is challenging.
method Used a multi-layer perceptron to estimate OU process parameters compared to traditional methods like Kalman filter and maximum likelihood estimation.
result Deep learning method outperforms traditional methods in parameter estimation of the OU process.
Deep learning models outperform traditional methods in stock price prediction.
problem Improving stock price prediction accuracy using deep learning.
method Comparative analysis of deep learning models (LSTM, GRU) and traditional methods (ARIMA, ARMA) on historical data.
result Deep learning models, particularly LSTM, outperform traditional methods in predicting stock prices across different time horizons.
We find a convex model for traditional nonlinear regression under L2 loss.
problem Nonlinear regression under L2 loss with non-convex optimization.
method Showed a convex nonlinear regression model for least squares problem.
result Existence of a convex model simplifies training complex systems.
This paper deals with a semi-classical limit (Theorem 1) by using traditional mathematical methods, and shows a Hopf theorem as a corollary. A formal discussion of it may be found in [7].
This chapter reviews classic regression methods and their evolution to physics-informed approaches.
problem Finding relationships between variables using regression.
method Introduces traditional and physics-informed regression methods, linking them to computational science.
result Regression methods have evolved from purely statistical to incorporating physical knowledge.
Little is known about how different types of advertising affect brand attitudes. We investigate the relationships between three brand attitude variables (perceived quality, perceived value and recent satisfaction) and three types of advertising (national traditional, local traditional and digital). The data represent t…
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
problem Adoption and integration of decentralized finance (DeFi) in financial services.
method Survey analysis using New Institutional Economics and Dynamic Capabilities Theory.
result Experts expect adoption of DeFi to rise from negligible to 43% by 2034, with traditional finance likely to embrace it.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
problem Understanding the dynamics between e-commerce and traditional retail during the pandemic.
method Developed an evolutionary game model to study consumer-producer interactions on e-commerce platforms.
result Investment in logistics and warehouses in e-commerce led to faster delivery and consumer trends.
With the rise of social media like Twitter and of software distribution platforms like app stores, users got various ways to express their opinion about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software de…
Decision analytics commonly focuses on the text mining of financial news sources in order to provide managerial decision support and to predict stock market movements. Existing predictive frameworks almost exclusively apply traditional machine learning methods, whereas recent research indicates that traditional machine…
The Interactive Minority Game (IMG) is an online version of the traditional Minority Game in which human players can enter into competition with the traditional computer-controlled agents. Through the rich (and, importantly, analytically understood) behaviour of the MG, we can explore humans' behaviour in different kin…
ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.
problem Impact of Bitcoin ETF approval on Bitcoin's relationships with traditional assets.
method Rolling correlation analysis, Chow tests, and DCC-GARCH models.
result Bitcoin's correlation with equities increased significantly post-ETF approval, while its relationship with gold stabilized and remained negatively correlated with fiat currencies.
New neural networks combine additive regression with traditional architectures.
problem Performance limitations and high parameter requirements of traditional neural networks.
method Introduce hybrid deep additive neural networks with simpler activation and basis functions.
result Hybrid neural networks achieve better performance with fewer parameters.
Scaling properties of the BUX index are similar to those observed in other parts of the world. The main difference is that the traditional quantities like volatility, growth and autocorrelation of returns follows more closely the assumptions of the traditional stock market theory developed by Bachelier and by Black and…
Pakistan examines digital mergers using traditional competition tools.
problem Regulating digital mergers in a developing country.
method Empirical comparative analysis of CCP's M&A decisions.
result CCP uses same decision factors for digital and traditional M&As.
Blockchain protocol improves traditional mutual funds with performance fees and investor protection.
problem Operational issues and performance fees in traditional mutual funds.
method Developed a blockchain protocol that integrates features of mutual funds and hedge funds.
result Blockchain can simplify performance fee calculations and protect investors.
Study examines stylized facts in DEX markets vs. traditional exchanges.
problem Comparing stylized facts in decentralized exchanges (DEXs) vs. traditional markets.
method Empirical analysis of 24 most active Uniswap v3 pools.
result New statistical regularities in DEX markets, linked to market structure and activity.
Automl is the key technology for machine learning problem. Current state of art hyperparameter optimization methods are based on traditional black-box optimization methods like SMBO (SMAC, TPE). The objective function of black-box optimization is non-smooth, or time-consuming to evaluate, or in some way noisy. Recent y…
Machine learning models outperform traditional option pricing models.
problem Improving option pricing accuracy using complex models.
method Evaluation of machine learning (NN, RF, CatBoost) and traditional models (Black-Scholes, Heston) on synthetic and real data.
result Machine learning models outperform traditional models in predicting option prices.
New trading strategy beats traditional grid in crypto markets.
problem Low expected return of traditional grid trading strategy.
method Dynamic Grid Trading (DGT) strategy that adapts to market conditions.
result DGT strategy outperforms traditional grid and buy-and-hold strategies.
Paper models demand and solvency for index insurance, combining traditional and measurable index-based coverage.
problem Reducing protection gaps for emerging risks.
method Develops a model for demand and solvency conditions, combining traditional and index-based insurance.
result Deduces a product that benefits from both traditional and index-based insurance approaches.
Math and dance blend in choreographer's research.
problem Exploring intersections between dance and mathematics.
method Choreographic practice and mathematical concepts.
result Examples of fractals, braids in choreography.
RL models outperform traditional methods in certain market conditions.
problem Traditional portfolio management methods rely on accurate forecasts and do not incorporate specific investor preferences.
method Deep reinforcement learning with specific investor preferences incorporated into reward functions, realistic transaction costs modelled.
result RL models can significantly outperform traditional methods in upward trending markets, but not in sideways trending markets.
Study compares traditional and machine learning methods for handling missing data in longitudinal studies.
problem Handling missing Not at Random (MNAR) and nonnormal data in longitudinal research.
method Monte Carlo simulations to assess six missing data techniques.
result FIML is most effective for MNAR data, while TSRE excels for MAR data.
Combines model-based and model-free RL for better financial market performance.
problem Challenges of Reinforcement Learning in volatile financial markets.
method Adapts model-based RL with model-free RL, incorporating contextual signals and walk-forward analysis.
result Outperforms traditional financial models in various metrics.
Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to valu…
Thousands of first-millennium BCE ivory carvings have been excavated from Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan Tash) hundreds of miles from their Levantine production contexts. At present, their specific manufacture dates and workshop localities are unknown. Relying on subjective, …
Precise financial series predicting has long been a difficult problem because of unstableness and many noises within the series. Although Traditional time series models like ARIMA and GARCH have been researched and proved to be effective in predicting, their performances are still far from satisfying. Machine Learning,…
It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. However, due to the obscured and indescribable black box model of neural networks, it is very difficult to design its architecture, interpret its …
In portfolio analysis, the traditional approach of replacing population moments with sample counterparts may lead to suboptimal portfolio choices. I show that optimal portfolio weights can be estimated using a machine learning (ML) framework, where the outcome to be predicted is a constant and the vector of explanatory…
AI improves credit rating predictions over traditional methods.
problem Improving credit rating predictions for global corporate entities.
method Applying deep learning techniques, specifically neural networks with categorical embeddings, to a large dataset of corporate obligations.
result Deep learning models achieve adequate accuracy in predicting different credit rating classes.
Car following models have been widely applied and made remarkable achievements in traffic engineering. However, the traffic micro-simulation accuracy of car following models in a platoon level, especially during traffic oscillations, still needs to be enhanced. Rather than using traditional individual car following mod…
This paper proposes the use of wavelet methods to estimate U.S. core inflation. It explains wavelet methods and suggests they are ideally suited to this task. Comparisons are made with traditional CPI-based and regression-based measures for their performance in following trend inflation and predicting future inflation.…
Thurston's influence on French math traced and problems solved.
problem Major problems in French math traced back to Thurston.
method Overview and survey of Thurston's influence and results.
result French math problems rooted in Thurston's work.
Paper discusses flaws in traditional RL for lifelong learning.
problem Traditional RL fails to model lifelong learning systems.
method Simplified prototype of lifelong RL system.
result Insights into lifelong RL, showing traditional RL's limitations.
Cryptocurrencies are increasingly correlated with traditional financial markets.
problem Determining the independence of cryptocurrencies from traditional financial markets.
method High-frequency detrended cross-correlation analysis over various time scales and market periods.
result Cryptocurrencies have become more aligned with traditional financial markets, especially during bear phases.
Investors prioritize ESG in crypto-assets, showing higher exposure than traditional assets.
problem Understanding ESG preferences in crypto-assets and their investment behavior.
method A representative household finance survey in Austria to examine ESG preferences and crypto-investment exposure.
result ESG-conscious investors have higher exposure to crypto-assets compared to traditional asset classes.
In this article, we advance divide-and-conquer strategies for solving the community detection problem in networks. We propose two algorithms which perform clustering on a number of small subgraphs and finally patches the results into a single clustering. The main advantage of these algorithms is that they bring down si…
New framework improves classification accuracy using Pillai's trace and ULDA.
problem Traditional LDA's limitations in noise sensitivity and non-invertible matrices.
method Integrates Pillai's trace with ULDA for a unified classifier.
result Effective control of Type I error rates and improved classification accuracy.
This paper compares traditional econometric and contemporary machine/deep learning techniques for forecasting foreign exchange rates.
problem Accurate prediction of foreign exchange rates for investment purposes.
method Multivariate time series analysis using Vector Auto Regression, Support Vector Machine, and Recurrent Neural Networks.
result Contemporary machine/deep learning techniques outperform traditional econometric methods in forecasting foreign exchange rates.
Neural network outperforms traditional methods in chaotic dynamics classification.
problem Classifying chaotic and regular dynamics of the Chirikov standard map.
method Trained a convolutional neural network on finite-length trajectories compared to traditional Lyapunov exponent computation.
result Neural network outperforms traditional methods for short periods, converging faster and more robustly.
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input z to a sample x that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function…
Machine learning models outperform traditional econometric methods for forecasting term structure of government bonds
problem Forecasting the term structure of government bonds
method Combining traditional econometric models with neural network architectures
result Neural network models consistently outperform traditional models in both forecasting accuracy and portfolio performance
Study optimizes tree-based models for better alignment of predicted scores and actual probabilities.
problem Traditional calibration metrics fail to align predicted scores with actual probabilities when score distributions deviate from the underlying data.
method Optimizes tree-based models (Random Forest, XGBoost) using Kullback-Leibler (KL) divergence to minimize the difference between predicted and true probability distributions.
result Optimized tree-based models yield superior alignment between predicted scores and actual probabilities without significant performance loss.