A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Environmental stresses such as drought and heat can cause substantial yield loss in agriculture. As such, hybrid crops that are tolerant to drought and heat stress would produce more consistent yields compared to the hybrids that are not tolerant to these stresses. In the 2019 Syngenta Crop Challenge, Syngenta released…
A neural collaborative filtering method predicts corn hybrid yield performance.
A CNN-based method detects and counts corn kernels from images.
This appendix proves CORN's universal consistency. One of Bin's PhD thesis examiner (Special thanks to Vladimir Vovk from Royal Holloway, University of London) suggested that CORN is universal and provided sketch proof of Lemma 1.6, which is the key of this proof. Based on the proof in Gyprfi et al. [2006], we thus pro…
Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. This paper is the first to em…
This paper estimates VaR for corn and soybean markets using jump processes.
For the first time, we apply the wavelet coherence methodology on biofuels (ethanol and biodiesel) and a wide range of related commodities (gasoline, diesel, crude oil, corn, wheat, soybeans, sugarcane and rapeseed oil). This way, we are able to investigate dynamics of correlations in time and across scales (frequencie…
Paper presents a machine learning framework for corn yield forecasting.
Study shows post-COVID commodity futures returns and volatility changed for different products.
Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multiple standpoints, including impact on the crop commodity futures markets, formulation of agricultural policy, as well as crop insurance ratin…
Time and Sales of corn futures traded electronically on the CME Group Globex are studied. Theories of continuous prices turn upside down reality of intra-day trading. Prices and their increments are discrete and obey lattice probability distributions. A function for systematic evolution of futures trading volume is pro…
Portfolio selection is the central task for assets management, but it turns out to be very challenging. Methods based on pattern matching, particularly the CORN-K algorithm, have achieved promising performance on several stock markets. A key shortage of the existing pattern matching methods, however, is that the risk i…
Conversion of corn to ethanol in the US since 2005 has been a major cause of global food price increases during that time and has been shown to be ineffective in achieving US energy independence and reducing environmental impact. We make three key statements to enhance understanding and communication about ethanol prod…
Study measures risk spillovers between US and China's agricultural futures markets.
Machine learning improves measuring climate adaptation impacts.
Develops a binary tree model for option pricing with skew dynamics.
Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accurac…
Recent increases in basic food prices are severely impacting vulnerable populations worldwide. Proposed causes such as shortages of grain due to adverse weather, increasing meat consumption in China and India, conversion of corn to ethanol in the US, and investor speculation on commodity markets lead to widely differin…
Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield …
Recent droughts in the midwestern United States threaten to cause global catastrophe driven by a speculator amplified food price bubble. Here we show the effect of speculators on food prices using a validated quantitative model that accurately describes historical food prices. During the last six years, high and fluctu…
Proposes a new method for rank-consistent ordinal regression without weight-sharing constraints.
edPLS adds Gaussian noise to PLS regression to protect data privacy.
The paper examines spillovers between agriculture, crude oil, carbon, and climate markets.
Defines non-parabolic curves in spatial hybrid space with applications.
Risk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value …
Discussing hybrid models in Bayesian networks.
Expert augmentation improves hybrid model generalization.
In this work we present a new approach on studying dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have research the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generaliz…
We explore hybrid subgroups of certain non-arithmetic lattices in . We show that all of Mostow's lattices are virtually hybrids; moreover, we show that some of these non-arithmetic lattices are hybrids of two non-commensurable arithmetic lattices in .
Defines hybrid systems on principal bundles and studies impact effects.
Hybrid models forecast EPEC energy spot prices.
New method sparsifies hybrid neural ODEs for better performance and stability.
Hybrid models combine interpretable and complex models for better performance and control.
A hybrid ASR system using conformer architecture improves word-error-rate and training speed.
The paper introduces FMCI and hybrid decoding for hidden Markov models.
In the present paper, a fuzzy logic based method is combined with wavelet decomposition to develop a step-by-step dynamic hybrid model for the estimation of financial time series. Empirical tests on fuzzy regression, wavelet decomposition as well as the new hybrid model are conducted on the well known index fin…
Hybrid model improves music source separation by 1.4 dB.
Develops numerical methods for PDEs on hypergraphs and networks.
Paper proposes a method to estimate scientific parameters in hybrid models without relying on model architecture.
Functional PLS improves prediction and inference for scalar responses from functional predictors.
HyBO optimizes hybrid structures using diffusion kernels.
Mathematical analysis of Riemann surfaces and their moduli spaces using hybrid Laplacians.
Novel hybrid method for Bayesian network structure learning reduces computational time without sacrificing accuracy.
Hybrid systems are characterized by having an interaction between continuous dynamics and discrete events. The contribution of this paper is to provide hybrid systems with a novel geometric formulation so that controls can be added. Using this framework we describe some new global controllability tests for hybrid contr…
A hybrid model for Bayesian optimization handles mixed variables using MCTS for categorical and GP for continuous.
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
Regularization is an important component of predictive model building. The hybrid bootstrap is a regularization technique that functions similarly to dropout except that features are resampled from other training points rather than replaced with zeros. We show that the hybrid bootstrap offers superior performance to dr…
MES-LSTM hybrid method improves multivariate time series forecasting and mortality modeling.