21 papers · ranked by Valyu relevance
Jacinta Chan Phooi M’ng, Mohammadali Mehralizadeh, Zhong-Ke Gao
The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting…
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…
Rick Steinert, Florian Ziel
Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the…
Jie Wang, Jun Wang
The crude oil futures prices forecasting is a significant research topic for the management of the energy futures market. In order to optimize the accuracy of energy futures prices prediction, a new hybrid model is established in this paper which combines wavelet packet decomposition (WPD) based on long short-term…
Fearghal Kearney, Han Lin Shang
Accurately forecasting the price of oil, the world's most actively traded commodity, is of great importance to both academics and practitioners. We contribute by proposing a functional time series based method to model and forecast oil futures. Our approach boasts a number of theoretical and practical advantages…
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…
Jozef Baruník, Barbora Malinská
The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested…
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…
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…
Owen L. Petchey, Mikael Pontarp, Thomas M. Massie, Sonia Kéfi + 14 more
Forecasts of how ecological systems respond to environmental change are increasingly important. Sufficiently inaccurate forecasts will be of little use, however. For example, weather forecasts are for about one week into the future; after that they are too unreliable to be useful (i.e., the forecast horizon is about…
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…
Ethan P. White, Glenda M. Yenni, Shawn D. Taylor, Erica M. Christensen + 3 more
Most forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts…
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…
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…
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…
Frank Pennekamp, Alison C. Iles, Joshua Garland, Georgina Brennan + 15 more
Realized predictability is highest when the chosen forecasting model exploits all the active information contained in a time series. For illustration, we forecast the oscillating abundance of a laboratory ciliate population (57) with three different approaches (Figure 1): i) the mean of the time series (a model which…
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…
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)…