Download or read book A Blending Ensemble Learning Model for Crude Oil Price Prediction written by Mahmudul Hasan and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given that the price of crude oil is driven by a number of factors with varying frequency, it is difficult to accurately capture its behavior, which in turn leads to challenges in forecasting. Moreover, different mechanisms of fluctuations have been observed at different time series periods. To efficiently capture these diverse fluctuation profiles, we propose to combine heterogenous predictors for predicting the crude oil price. Specifically, a forecasting model is developed using blended ensemble learning is developed that combines various machine learning methods, including linear regression, k-nearest neighbor regression, regression trees, support vector regression, and ridge regression. Brent and WTI crude oil data at various time series frequencies are used to validate the proposed blending ensemble learning approach. To show the effectiveness of the proposed model, its performance is compared with existing individual and ensemble learning methods used for crude oil price prediction, such as lasso regression, bagging lasso regression, boosting, random forest, and support vector regression. We show that our proposed blending ensemble learning model dominates the existing forecasting models in terms of forecasting errors. The proposed model exhibits a good prediction performance for both short- and long-term forecasting horizons, which is beneficial to stakeholders and related industries that depend on this energy source.

Download or read book What Can Be Learned from the Historical Trend of Crude Oil Prices An Ensemble Approach to Crude Oil Price Forecasting written by Mingchen Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crude oil price series are nonlinear and highly volatile, making it difficult to obtain satisfactory performance for traditional statistical-based forecasting methods. To improve prediction accuracy, this study proposes a novel learning paradigm by integrating the trajectory similarity method with machine learning models based on the decomposition-ensemble framework. In the proposed learning paradigm, raw data of international crude oil prices are first decomposed using variational mode decomposition (VMD), after which, using sample entropy (SE), the resulting essential modal functions are divided into high and low frequencies. The process aims to reorganize the data by using the forecasting properties of different models. Finally, to obtain the final forecasting results, two models, i.e., the trajectory similarity method (TS) and artificial neural networks (ANN), are applied to predict and sum up the low and high-frequency subseries, respectively. As sample data for validation, this study selected the international crude oil price series of West Texas Intermediate (WTI) and Brent. Experimental results showed that the proposed VMD-SE-TS/ANN learning paradigm significantly outperforms all other benchmark models, including the single models without decomposition and the hybrid models with decomposition. The proposed approach performs best in different evaluation metrics and statistical tests under different horizons, indicating that the proposed VMD-SE-TS/ANN learning paradigm is effective and robust in crude oil price forecasting.

Download or read book Machine Learning Approach for Crude Oil Price Prediction written by Siti Norbaiti binti Abdullah and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting commodity prices using long short term memory neural networks written by Ly, Racine and published by Intl Food Policy Res Inst. This book was released on 2021-02-10 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well with the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) or the naïve models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower, respectively, for cotton. For oil, the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.

Download or read book A Novel Multivariate Decomposition Ensemble Model with News Text for Crude Oil Price Forecasting written by Zhengling Zhao and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting crude oil prices is crucial for every individual and even the entire country. Previous studies have encountered difficulties in forecasting highly nonlinear crude oil prices, especially when conflicts, wars, and other irregular events occur. In light of this, this study proposes a novel multivariate decomposition ensemble model with news text for crude oil price forecasting, which mainly consists of four steps. First, data fusion of multivariate forecasters is performed. Second, the crude oil price and its forecasters are decomposed and reconstructed using multivariate empirical mode decomposition (MEMD) and sample entropy (SE), respectively. Thereafter, the effective forecasters are screened from the reconstruction subcomponents of forecasters through the Granger causality test. Finally, the crude oil price is forecasted using a hybrid forecasting technique, and the validity of the proposed model is evaluated from different perspectives. The empirical results indicate that the proposed model achieves excellent performance in forecasting the West Texas Intermediate weekly spot price.

Download or read book Crude Oil Price Forecasting Using Machine Learning written by Lubna Gabralla and published by . This book was released on 2016-09-15 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting Accuracy of Crude Oil Futures Prices written by Mr.Manmohan S. Kumar and published by International Monetary Fund. This book was released on 1991-10-01 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper undertakes an investigation into the efficiency of the crude oil futures market and the forecasting accuracy of futures prices. Efficiency of the market is analysed in terms of the expected excess returns to speculation in the futures market. Accuracy of futures prices is compared with that of forecasts using alternative techniques, including time series and econometric models, as well as judgemental forecasts. The paper also explores the predictive power of futures prices by comparing the forecasting accuracy of end-of-month prices with weekly and monthly averages, using a variety of different weighting schemes. Finally, the paper investigates whether the forecasts from using futures prices can be improved by incorporating information from other forecasting techniques.

Download or read book EEMD CNN BiLSTM QR Enabled Probability Density Forecasts for Crude Oil Price written by Yanmei Huang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The price of crude oil has been subject to periodical fluctuations as a consequence of changes in seasonal demand and supply, as well as weather, natural disasters, and global political unrest. Accurate forecast of crude oil prices is of utmost importance for decision-makers and industry players in the energy sector. Despite this, the volatility of crude oil prices contributes to the uncertainty of the energy industry, which was particularly challenging following the recent global spread of the COVID-19 pandemic as well as Russia-Ukraine conflicts. This paper aims to propose a hybrid modeling framework to deal with the volatility of crude oil prices, employing several well-established data analytics such as ensemble empirical mode decomposition (EEMD), convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM) integrated with quantile regression (QR), named as EEMD-CNN-BiLSTM-QR. Two sets of real-world data of crude oil prices from the West Texas Intermediate (WTI) and the Brent Crude Oil markets were employed to validate the EEMD-CNN-BiLSTM-QR hybrid modeling framework. An in-depth analysis was carried out with the prediction accuracy being calculated while the probability density forecast remains uncertain. The findings of this study demonstrated that the proposed EEMD-CNN-BiLSTM-QR modeling framework is superior to other tested models in terms of its ability to forecast crude oil prices. The novelty of this study stems mostly from the use of QR, which allows for the description of the conditional distribution of predicted variables and the extraction of more uncertain information for probability density forecast.

Download or read book Application of Markov Model in Crude Oil Price Forecasting written by Nuhu Isah and published by . This book was released on 2017 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM) approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.

Download or read book Crude Oil Price Prediction written by Yifeng Zhu and published by . This book was released on 2016 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we propose linear and nonparametric models to predict one month, three months, six months, one year, eighteen months and two years ahead crude oil price in out-of-sample background. Mainly, our forecast depends on three predictor variables, the change in crude oil inventories, its previous prices and product spread. By employing mean-squared prediction error (MSPE) and stochastic dominance (SD) tests, we find that the prediction result of our nonparametric models is significantly better than the random walk model, while the corresponding linear models' performance is better than the random walk model only for longer horizon forecasts (one to two years). In General, for the sample period from 1995.1 to 2015.4, the conclusion is that our model applying nonparametric estimation always outperforms all other models in different horizon forecasting. And for the nonparametric model including all three predictors, we document MSPE reduction as high as 62.6% and directional accuracy ratio as high as 77.5% at the two years horizon compared to the random walk model.

Download or read book Comparative Analysis and Modification of Practically Used Crude Oil Price Forecasting Models written by Artem Prokopovich and published by . This book was released on with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper aims to study whether adding variables to autoregressive crude oil forecasting model improves its forecast accuracy. The academic literature has been analyzed with the purpose of identifying possible variables that could improve the model. The relationship between crude oil prices and variables in the categories supply, demand, inventory, and economy have been analyzed by the methods of the Granger causality test. The autoregressive forecasting models of crude oil have been modified with additional variables and the forecast accuracy was measured by root-mean-square deviation. The results obtained showed that crude oil forecast models that are only based on its historical prices are better in most cases, however, adding specific variables can improve its forecast accuracy.

Download or read book The Effect of Performance Metrics and Sentiment Scores on Selecting Oil Price Prediction Models written by Christian Haas and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predicting crude oil prices is an important yet challenging forecasting problem due to various influencing quantitative and qualitative factors. To address the growing number of potential prediction models and model parameters that researchers and practitioners need to consider during model selection, we suggest a systematic comparison of alternative prediction models and variables. In this article, we provide a novel perspective on oil price prediction models by comparing a variety of different forecasting models and considering both their statistical and financial performance. To assess the usefulness in a practical setting, we evaluate the predictions in a simulation of a simple trading strategy. We show that the ranking of different approaches depends on the selected evaluation metric and that small differences between models in one evaluation metric can translate into large differences in another metric. Finally, we show that including qualitative information in the prediction models through sentiment analysis can yield both statistical and financial performance improvements.

Download or read book Forecasting the Real Prices of Crude Oil written by Yudong Wang and published by . This book was released on 2015 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting oil prices has been of great interests for macroeconomists in the recent years. Our article contributes to this strand of the literature by using a dynamic model averaging (DMA) method to improve forecasting accuracy of real oil prices. The advantage of DMA is that the method combines models in a dynamic way using two forgetting factors to approximate the evolution of model parameters and model switching probabilities, respectively. Our empirical results show that DMA generates more accurate forecasts than the no-change forecasts at the relatively longer horizons. At a horizon of 12 months, the reduction of mean squared prediction error is as high as 30% and the accuracy of directional forecasts increases as high as 71%. It is also found that DMA performs better than Bayesian model averaging, the commonly-used mean combination of forecasts, and more sophisticated individual models such as a time-varying dimension model for the horizons of 3 and 12 months.

Download or read book Oil Market Efficiency Under a Machine Learning Perspective written by Athanasia Dimitriadou and published by . This book was released on 2018 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting commodities and especially oil prices has attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables often used in the relevant literature and through a selection process we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

Download or read book Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks written by Jozef Baruník and published by . This book was released on 2015 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.

Download or read book A Framework for Multivariate Time Series Prediction of Crude Oil Prices Based on Artificial Neural Networks written by Mohammad Isa Irawan and published by . This book was released on 1998 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Performance Analysis of Machine Learning Techniques in Stock Price Prediction written by Hasan Al-Quaid and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market trends are of great interest to investors and corporations worldwide. The global financial system is intricately interconnected with the stock market, playing a central role in driving economic activity. In today's interconnected world, trading stocks has become a popular and accessible means for individuals and entities to generate income. Numerous academic researchers have explored the use of Artificial Intelligence (AI) for stock prediction and have claimed that their models can accurately forecast stock performance. The issue is that many of these studies rely on a single data source, namely, daily stock data and cannot predict future stock prices, more than 1 or 2 days, with a large degree of success. Additionally, the single data source may be influenced by a multitude of economic factors as well as public sentiment, which is the most significant. In this research paper, several of these AI models are tested to evaluate their claims regarding stock prediction capabilities. Based on our experiments utilizing AI models and the results gathered, it was concluded that it was not possible to predict future stock prices using one method alone. Therefore, in order to provide a greater accuracy in predicting future stocks, the use of an ensemble approach was proposed. While many researchers build their ensemble models by combining various Artificial Neural Network models with sentiment analysis. We have suggested a different approach using other kinds of AI models, along with enhancements to traditional sentiment analysis techniques.