21 papers · ranked by Valyu relevance
Peng Chen, Andrew Vivian, Cheng Ye
In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and…
Abhijit Gupta
Commodity futures price volatility creates significant economic challenges, necessitating accurate multihorizon forecasting. Predicting these prices is complicated by diverse interacting factors (macroeconomic, supply/demand, geopolitical). Current models often lack transparency, limiting strategic use. This paper…
Nikolas Michael, Mihai Cucuringu, Sam Howison
†Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK ‡Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Rd, Oxford OX2 6GG ¶ Oxford-Man Institute of Quantitative Finance, University of Oxford ⊙ The Alan Turing Institute, John Dodson House, 96 Euston Rd…
Weifang Mao, Pin Liu, Jixian Huang, Brian Lucey + 3 more
'Xueyong Liu' 'Xinya Wang'] The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in…
Yan Li, Dezhi Liu, Ruobin Gao, Yang Yu + 1 more
Accurate forecasting of copper futures prices is crucial for risk management and investment decisions. However, existing approaches primarily rely on historical prices and incorporate behavioral signals without a unified modeling framework. To address this limitation, we propose MBTI-Net (Multi-source…
Anisha Saha, Adam Jatowt
Supporting Future Event Prediction Authors: ['Anisha Saha' 'Adam Jatowt'] Future Event Prediction (FEP) is an essential activity whose demand and application range across multiple domains. While traditional methods like simulations, predictive and time-series forecasting have demonstrated promising outcomes, their…
Le Wang, Boyuan Zhang
Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds with modern…
Zian Wang, Xinyi Lu
Learning Authors: ['Zian Wang' 'Xinyi Lu'] This paper investigates the forecasting performance of COMEX copper futures realized volatility across various high-frequency intervals using both econometric volatility models and deep learning recurrent neural network models. The econometric models considered are GARCH and…
Ying Peng, Yifan Zhang, Xin Wang
In this paper, we mainly focus on the prediction of short-term average return directions in China's high-frequency futures market. As minor fluctuations with limited amplitude and short duration are typically regarded as random noise, only price movements of sufficient magnitude qualify as statistically significant…
Xue Chen, Yan Hu, Dimitris Kugiumtzis
This paper is concerned with the unsolved issue of how to accurately predict the financial market volatility. We propose a novel volatility prediction method for stock index futures prediction based on LSTM, PCA, stock indices and relevant futures. Inspired by the recent advancement of deep learning methodology, six…
Yankai Sheng, Ding Ma, Jae Woo Lee
With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot-futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly…
Lee Mason, Amy Berrington de Gonzalez, Montserrat Garcia-Closas, Stephen J. Chanock + 3 more
'Stephen J. Chanock' 'Blànaid Hicks' 'Jonas S. Almeida' 'Zakariya Yahya Algamal'] Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it…
Penelope Jones, Ulrich Stimming, Alpha Lee
Accurate forecasting of lithium-ion battery performance is important for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the R&D setting where cells are subjected to the same usage patterns, yet in practice there is great variability…
Christopher J. Brown, Christina Buelow, Rick D. Stuart-Smith, Neville Barrett + 2 more
Cascading human pressures and environmental change are affecting the natural dynamics of animal populations. Forecasting population abundances from time-series data provides an important avenue for testing competing ecological theories, and for supporting conservation planning and sustainable use, yet changing system…
Joung Min Choi, Monjura Afrin Rumi, Peter J. Vikesland, Amy Pruden + 1 more
The global spread of antibiotic resistance presents a significant threat to human, animal, and plant health. Metagenomic sequencing is increasingly being utilized to profile antibiotic resistance genes (ARGs) in various environments, but presently a mechanism for predicting future trends in ARG occurrence patterns is…
Takeshi Honda, Chinatsu Kozakai
Forecasting encounters between humans and large carnivores has largely relied on mechanistic models driven by causal factors such as food resources and weather. However, for short-term forecasting these approaches implicitly require unrealistically detailed real-time data on many covariates and an almost complete…
Hanna Pankka, Jaakko Lehtinen, Risto J. Ilmoniemi, Timo Roine
Forecasting electroencephalography (EEG) signals, i.e., estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain–computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance…
Authors not listed
Accurately modeling the dynamics of open quantum systems is critical for advancing quantum technologies, yet traditional methods often struggle with balancing accuracy and efficiency. Machine learning (ML) offers a promising alternative, particularly through recursive models that predict system evolution based on the…
Zahrah Fayez Althobaiti
Predicting the economic implications of gas emissions and their repercussions is criti-cal to policymakers, especially given the current increasing trend in volume. Therefore, study on gas emission prediction is required. A hybrid model is proposed for forecasting CO_2_ emissions of Bahrain (BH) in this study. Singular…
Gagan Acharya, Erin Conrad, Kathryn A. Davis, Erfan Nozari
Extensive research over the past two decades has focused on identifying a preictal period in scalp as well as intracranial EEG (iEEG). This has led to a plethora of seizure prediction and forecasting algorithms which have reached only moderate success on curated and pre-segmented EEG datasets (accuracy/AUC ≳ 0.8).…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…