☆ 4.7 Article

A dynamic clustering ensemble learning approach for crude oil price forecasting

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2023)

Related references

Note: Only part of the references are listed.

Article Operations Research & Management Science

Multi-objective optimization of crude oil-supply portfolio based on interval prediction data

Xiaolei Sun et al.

Summary: This paper investigates the optimization of crude oil-supply composition and introduces the decomposition hybrid interval prediction method and a multi-objective programming model. By setting optimization parameters and risk preference factors, the study achieves the minimum cost and risk of importing crude oil. The results show that the decomposition hybrid prediction method outperforms single prediction methods, and increasing the optimization parameter significantly reduces costs and risks. The total cost of imported crude oil fluctuates sharply, while the total risk decreases with the increase of risk preference factors. The fluctuation of price and risk adjustment factors affects the optimization scheme for oil-supply portfolio.

ANNALS OF OPERATIONS RESEARCH (2022)

Add to Collection

Article Economics

Crude oil price forecasting incorporating news text

Yun Bai et al.

Summary: The paper introduces two novel indicators of topic and sentiment for short and sparse text data to improve crude oil forecasting performance. Empirical experiments show that AdaBoost.RT with the proposed text indicators outperforms other benchmarks in forecasting performance.

INTERNATIONAL JOURNAL OF FORECASTING (2022)

Add to Collection

Article Economics

What matters when developing oil price volatility forecasting frameworks?

Panagiotis Delis et al.

Summary: The study found that factors influencing the accuracy of forecasts differ for realized and implied oil price volatility measures. For realized volatility, using a TVP estimation procedure and information obtained from semi variance measures can produce significantly superior forecasts. On the other hand, for implied volatility, the TVP estimation procedure seems to perform better.

JOURNAL OF FORECASTING (2022)

Add to Collection

Article Economics

A novel text-based framework for forecasting agricultural futures using massive online news headlines

Jianping Li et al.

Summary: The study proposes a text mining and sentiment analysis-based framework for forecasting agricultural futures prices, which can identify and quantify factors affecting prices through massive online news headlines. Empirical tests show that the framework performs significantly better than the benchmark model in medium and long-term forecasting.

INTERNATIONAL JOURNAL OF FORECASTING (2022)

Add to Collection

Article Computer Science, Information Systems

Bayesian optimization based dynamic ensemble for time series forecasting

Liang Du et al.

Summary: In this paper, a Bayesian optimization-based dynamic ensemble (BODE) method is proposed for time series forecasting. The BODE method combines ten different model candidates and uses a model-based Bayesian optimization algorithm for combination hyperparameter tuning. The method demonstrates robust performance and better generalization capability.

INFORMATION SCIENCES (2022)

Add to Collection

Article Environmental Studies

A dynamic ensemble learning with multi-objective optimization for oil prices prediction

Jun Hao et al.

Summary: This paper proposes a dynamic time-varying weight ensemble prediction model with multi-objective programming, which improves the prediction performance of oil prices by introducing the metabolism mechanism and sliding window technology. Experimental results demonstrate that the proposed model has excellent prediction capability and can serve as an effective forecasting tool.

RESOURCES POLICY (2022)

Add to Collection

Article Business, Finance

Oil price volatility predictability based on global economic conditions

Yangli Guo et al.

Summary: This study aims to examine the forecasting ability of five global economic activity proxies for WTI crude oil price volatility and construct a new index to improve the accuracy of WTI crude oil price volatility forecasts. The results show that the Global Economic Conditions Index (GECON) and partial least squares (PLS) model have strong predictive power for WTI crude oil price volatility.

INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS (2022)

Add to Collection

Article Economics

Time-varying model averaging?

Yuying Sun et al.

Summary: This paper proposes a novel time-varying jackknife model averaging (TVJMA) estimator, which selects optimal time-varying combination weights by minimizing a local jackknife criterion. The research shows that the TVJMA estimator is asymptotically optimal in achieving the lowest possible local squared error loss in a class of time-varying model averaging estimators.

JOURNAL OF ECONOMETRICS (2021)

Add to Collection

Article

Employing stacked ensemble approach for time series forecasting

Nonita Sharma et al.

International Journal of Information Technology (Singapore) (2021)

Add to Collection

Article Energy & Fuels

A novel method for online real-time forecasting of crude oil price

Yuan Zhao et al.

Summary: This study proposes a new method for crude oil price forecasting by decomposing price sequences and establishing a hybrid model for point prediction and interval prediction, ultimately forming a predictor capable of online real-time prediction. Empirical results demonstrate that the method outperforms other competing models in terms of accuracy and robustness.

APPLIED ENERGY (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Optimal selection of heterogeneous ensemble strategies of time series forecasting with multi-objective programming

Jianping Li et al.

Summary: This paper proposes a heterogeneous ensemble forecasting model with multi-objective programming for nonlinear time series, and validates it using the Baltic Dry Index's time series data. Experimental results demonstrate the model's superior robustness in conducting out-of-sample predictions under different lead times.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Time series forecasting based on echo state network and empirical wavelet transformation

Ruobin Gao et al.

Summary: The Echo State Network (ESN) is a reservoir computing structure with randomly generated hidden layers for rapid learning and extrapolation. Utilizing wavelet transformations to extract features and eliminate noise has been proposed to enhance predictive accuracy. A two-stage predictive algorithm combining empirical wavelet transformations (EWT) and ESN has shown superior performance compared to other models on twelve public datasets with different features.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Article Automation & Control Systems

Forecasting crude oil price with a new hybrid approach and multi-source data

Yifan Yang et al.

Summary: This study incorporates traditional economic data and GSVI data to improve forecasting accuracy and proposes a new hybrid approach. Empirical results show that GSVI data outperforms economic data in level forecasting accuracy, while the hybrid data combines their advantages for the best performance. At the method level, the divide and conquer strategy demonstrates better forecasting performance.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Add to Collection

Article Business

A novel multiscale forecasting model for crude oil price time series

Ranran Li et al.

Summary: This study explores a novel multiscale hybrid paradigm to estimate crude oil prices, which decomposes the price into simple models to improve accuracy in forecasting complex time series.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2021)

Add to Collection

Article Operations Research & Management Science

Forecasting mid-price movement of Bitcoin futures using machine learning

Erdinc Akyildirim et al.

Summary: The study found that machine learning algorithms outperformed traditional models in predicting mid-price movement for Bitcoin futures prices, especially during the COVID-19 turmoil.

ANNALS OF OPERATIONS RESEARCH (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Forecasting China's sovereign CDS with a decomposition reconstruction strategy

Jianping Li et al.

Summary: This paper introduces a novel hybrid ensemble forecasting framework for predicting sovereign CDS using CEEMDAN decomposition and reconstruction strategy. Empirical results demonstrate that the model outperforms benchmark models in terms of forecasting accuracy, indicating its potential as an effective forecasting tool.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Article Economics

Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals

Erick Meira et al.

Summary: The study introduces a new method of model selection to enhance the predictive power of automated exponential smoothing routines using prediction intervals, along with a pruning strategy to improve accuracy. Empirical experiments show that these simple methods substantially improve forecasting accuracy.

INTERNATIONAL JOURNAL OF FORECASTING (2021)

Add to Collection

Article Business

Impact of Covid-19 outbreak on Turkish gasoline consumption

Bekir Oray Gungor et al.

Summary: The study found that the Covid-19 outbreak had a negative impact on Turkish gasoline consumption, with existing forecasting models performing poorly after the outbreak. Adding volatility improved forecast accuracy, as the outbreak led to an increase in consumption volatility. Therefore, policies targeting volatility can help mitigate adverse effects on market participants, tax revenues, and vulnerable groups during similar shocks.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2021)

Add to Collection

Editorial Material Energy & Fuels

Non-market forces significantly affect oil prices

Robert. K. Kaufmann et al.

NATURE ENERGY (2020)

Add to Collection

Article Energy & Fuels

Oil price regimes and their role in price diversions from market fundamentals

Robert. K. Kaufmann et al.

NATURE ENERGY (2020)

Add to Collection

Article Multidisciplinary Sciences

Leverage electron properties to predict phonon properties via transfer learning for semiconductors

Zeyu Liu et al.

SCIENCE ADVANCES (2020)

Add to Collection

Article Computer Science, Information Systems

Annual and Non-Monsoon Rainfall Prediction Modelling Using SVR-MLP: An Empirical Study From Odisha

Xiaobo Zhang et al.

IEEE ACCESS (2020)

Add to Collection

Article Economics

A detailed look at crude oil price volatility prediction using macroeconomic variables

Nima Nonejad

JOURNAL OF FORECASTING (2020)

Add to Collection

Article Multidisciplinary Sciences

How to detect high-performing individuals and groups: Decision similarity predicts accuracy

R. H. J. M. Kurvers et al.

SCIENCE ADVANCES (2019)

Add to Collection

Article Economics

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Yan-ran Ma et al.

JOURNAL OF FORECASTING (2019)

Add to Collection

Article Thermodynamics

Forecasting crude oil price: Does exist an optimal econometric model?

Vinicius Phillipe de Albuquerquemello et al.

ENERGY (2018)

Add to Collection

Article Management

Deep learning with long short-term memory networks for financial market predictions

Thomas Fischer et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2018)

Add to Collection

Article Multidisciplinary Sciences

Bayesian model selection for complex dynamic systems

Christoph Mark et al.

NATURE COMMUNICATIONS (2018)

Add to Collection

Article Economics

A deep learning ensemble approach for crude oil price forecasting

Yang Zhao et al.

ENERGY ECONOMICS (2017)

Add to Collection

Article Automation & Control Systems

A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting

Lean Yu et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2016)

Add to Collection

Article Management

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms-Support vector regression forecast combinations

Georgios Sermpinis et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2015)

Add to Collection

Article Operations Research & Management Science

A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting

Ling Tang et al.

ANNALS OF OPERATIONS RESEARCH (2015)

Add to Collection

Article Statistics & Probability

changepoint: AnRPackage for Changepoint Analysis

Rebecca Killick et al.

Journal of Statistical Software (2015)

Add to Collection

Article Management

Forecasting the volatility of crude oil futures using intraday data

Benoit Sevi

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2014)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Robust joint modeling of mean and dispersion through trimming

N. M. Neykov et al.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2012)

Add to Collection

Article Statistics & Probability

Weighted-mean trimming of multivariate data

Rainer Dyckerhoff et al.

JOURNAL OF MULTIVARIATE ANALYSIS (2011)

Add to Collection

Article Economics

Simple robust averages of forecasts: Some empirical results

Victor Richmond R. Jose et al.

INTERNATIONAL JOURNAL OF FORECASTING (2008)

Add to Collection

Article Multidisciplinary Sciences

Clustering by passing messages between data points

Brendan J. Frey et al.

SCIENCE (2007)

Add to Collection

Article Computer Science, Artificial Intelligence

Extreme learning machine: Theory and applications

Guang-Bin Huang et al.

NEUROCOMPUTING (2006)

Add to Collection

Article Statistics & Probability

Estimating the number of clusters in a data set via the gap statistic

R Tibshirani et al.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2001)

Add to Collection

© Peeref 2019-2024. All rights reserved.