Efficient Crude Oil Pricing Using a Machine Learning Approach

2021 ◽  
Author(s):  
Olugbenga Falode ◽  
Christopher Udomboso
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Value Chain ◽  
Daily Basis ◽  
Machine Learning Techniques ◽  
Statistical Efficiency ◽  
Hybrid Forecasting ◽  
Oil Pricing ◽  
The Individual ◽  
Hidden Neurons

Abstract Crude oil, a base for more than 6000 products that we use on a daily basis, accounts for 33% of global energy consumption. However, the outbreak and transmission of COVID-19 had significant implications for the entire value chain in the oil industry. The price crash and the fluctuations in price is known to have far reaching effect on global economies, with Nigeria hard. It has therefore become imperative to develop a tool for forecasting the price of crude oil in order to minimise the risks associated with volatility in oil prices and also be able to do proper planning. Hence, this article proposed a hybrid forecasting model involving a classical and machine learning techniques – autoregressive neural network, in determining the prices of crude oil. The monthly data used were obtained from the Central Bank of Nigeria website, spanning January 2006 to October 2020. Statistical efficiency was computed for the hybrid, and the models from which the proposed hybrid was built, using the percent relative efficiency. Analyses showed that the efficiency of the hybrid model, at 20 and 100 hidden neurons, was higher than that of the individual models, the latter being the best performing. The study recommends urgent diversification of the economy in order not for the nation to be plunged into a seemingly unending recession.

User Profiles for Personalizing Digital Libraries

2009 ◽  
pp. 149-158 ◽  
Author(s):  
Giovanni Semeraro ◽  
Pierpaolo Basile ◽  
Marco de Gemmis ◽  
Pasquale Lops
Keyword(s):  
Machine Learning ◽  
Digital Libraries ◽  
Machine Learning Techniques ◽  
Linguistic Knowledge ◽  
User Profiles ◽  
User Interests ◽  
Learning Techniques ◽  
Document Filtering ◽  
The Individual ◽  
Machine Readable

Exploring digital collections to find information relevant to a user’s interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the individual interests of the user. Algorithms designed to solve this problem base their relevance computations on user profiles in which representations of the users’ interests are maintained. The main focus of this chapter is the adoption of machine learning to build user profiles that capture user interests from documents. Profiles are used for intelligent document filtering in digital libraries. This work suggests the exploiting of knowledge stored in machine-readable dictionaries to obtain accurate user profiles that describe user interests by referring to concepts in those dictionaries. The main aim of the proposed approach is to show a real-world scenario in which the combination of machine learning techniques and linguistic knowledge is helpful to achieve intelligent document filtering.

From machine learning to sustainable taxation: GPS traces of trucks circulating in Belgium

2020 ◽  
Author(s):  
Arnaud Adam ◽  
Isabelle Thomas
Keyword(s):  
Machine Learning ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Spatial Effects ◽  
Space And Time ◽  
Learning Techniques ◽  
Other Information ◽  
Gps Traces ◽  
The Individual ◽  
Taxation System

<p>Transport geography has always been characterized by a lack of accurate data, leading to surveys often based on samples that are spatially not representative. However, the current deluge of data collected through sensors promises to overpass this scarcity of data. We here consider one example: since April 1<sup>st</sup> 2016, a GPS tracker is mandatory within each truck circulating in Belgium for kilometre taxes. Every 30 seconds, this tracker collects the position of the truck (as well as some other information such as speed or direction), leading to an individual taxation of trucks. This contribution uses a one-week exhaustive database containing the totality of trucks circulating in Belgium, in order to understand transport fluxes within the country, as well as the spatial effects of the taxation on the circulation of trucks.</p><p>Machine learning techniques are applied on over 270 million of GPS points to detect stops of trucks, leading to transform GPS sequences into a complete Origin-Destination matrix. Using machine learning allows to accurately classify stops that are different in nature (leisure stop, (un-)loading areas, or congested roads). Based on this matrix, we firstly propose an overview of the daily traffic, as well as an evaluation of the number of stops made in every Belgian place. Secondly, GPS sequences and stops are combined, leading to characterise sub-trajectories of each truck (first/last miles and transit) by their fiscal debit. This individual characterisation, as well as its variation in space and time, are here discussed: is the individual taxation system always efficient in space and time?</p><p>This contribution helps to better understand the circulation of trucks in Belgium, the places where they stopped, as well as the importance of their locations in a fiscal point of view. What are the potential modifications of the trucks routes that would lead to a more sustainable kilometre taxation? This contribution illustrates that combining big-data and machine learning open new roads for accurately measuring and modelling transportation.</p>

scholarly journals Vehicle Price Prediction using SVM Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 398-401
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Network Support ◽  
Java Application ◽  
Learning Techniques ◽  
The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

Algorithmic Machine Learning for Prediction of Stock Prices

2019 ◽  
pp. 142-169
Author(s):  
Mirza O. Beg ◽  
Mubashar Nazar Awan ◽  
Syed Shahzaib Ali
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Prediction Models ◽  
Stock Exchange ◽  
Moving Average ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Daily News ◽  
Trading Decisions

Stock markets and relevant entities generate enormous amounts of data on a daily basis and are accessible from various channels such as stock exchange, economic reviews, and employer monetary reports. In recent times, machine learning techniques have proven to be very helpful in making better trading decisions. Machine learning algorithms use complex logic to observe and learn the behavior of stocks using historical data which can be used to predict future movements of the stock. Technical indicators such as rolling mean, momentum, and exponential moving average are calculated to convert the data into meaningful information. Furthermore, this information can be used to build machine learning prediction models that learn different patterns in the data and make future predictions for accurate financial forecasting. Additional factors that are being used for stock prediction include social media influences and daily news on trading stocks. Considering these qualitative and quantitative features at the same time result in improved prediction models.

scholarly journals Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Thiago Christiano Silva ◽  
Benjamin Miranda Tabak ◽  
Idamar Magalhães Ferreira
Keyword(s):  
Machine Learning ◽  
Ad Hoc ◽  
Methodological Approach ◽  
Investment Decisions ◽  
Daily Basis ◽  
Investor Behavior ◽  
Machine Learning Techniques ◽  
Investment Strategies ◽  
Academic Degree ◽  
Momentum Trading

We model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high-frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these investment decisions over time. We then analyze whether these investment changes correlate with the IBOVESPA index. We find that investors decide their investment strategies using recent past price changes. There is some degree of heterogeneity in investment decisions. Overall, we find evidence of mean-reverting investment strategies. We also find evidence that female investors and higher academic degree have a less pronounced mean-reverting strategy behavior comparatively to male investors and those with lower academic degree. Finally, this paper provides a general methodological approach to mitigate potential biases arising from ad-hoc design decisions of discarding or introducing variables in empirical econometrics. For that, we use feature selection techniques from machine learning to identify relevant variables in an objective and concise way.

scholarly journals Early Detection of Cardiovascular Disease using Machine learning Techniques an Experimental Study

2020 ◽  
Vol 9 (3) ◽  
pp. 635-641
Keyword(s):  
Machine Learning ◽  
Proper Time ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Techniques ◽  
Entire Body ◽  
Time Data ◽  
Learning Techniques ◽  
Health Related

Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact our lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Therefore, the prediction of this disease is of prime importance as it will enable one to take a proper and needful approach at a proper time. Data mining and machine learning are taking out and refining of useful information from a massive amount of data. It is a basic and primary process in defining and discovering useful information and hidden patterns from databases. The flexibility and adaptability of optimization algorithms find its use in dealing with complex non -linear problems. Machine Learning techniques find its use in medical sciences in solving real health-related issues by early prediction and treatment of various diseases. In this paper, six machine learning algorithms are used and then compared accordingly based on the evaluation of performance. Among all classifiers, decision tree outperforms over the other algorithms with a testing accuracy of 97.29%.

scholarly journals Energy Profile Prediction of Milling Processes Using Machine Learning Techniques

2020 ◽  
pp. 1-11
Author(s):  
Matthias Mühlbauer ◽  
Hubert Würschinger ◽  
Dominik Polzer ◽  
Nico Hanenkamp
Keyword(s):  
Machine Learning ◽  
Power Consumption ◽  
Model Building ◽  
Milling Process ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Accurate Forecast ◽  
The Individual ◽  
Cutting Processes

AbstractThe prediction of the power consumption increases the transparency and the understanding of a cutting process, this delivers various potentials. Beside the planning and optimization of manufacturing processes, there are application areas in different kinds of deviation detection and condition monitoring. Due to the complicated stochastic processes during the cutting processes, analytical approaches quickly reach their limits. Since the 1980s, approaches for predicting the time or energy consumption use empirical models. Nevertheless, most of the existing models regard only static snapshots and are not able to picture the dynamic load fluctuations during the entire milling process. This paper describes a data-driven way for a more detailed prediction of the power consumption for a milling process using Machine Learning techniques. To increase the accuracy we used separate models and machine learning algorithms for different operations of the milling machine to predict the required time and energy. The merger of the individual models allows finally the accurate forecast of the load profile of the milling process for a specific machine tool. The following method introduces the whole pipeline from the data acquisition, over the preprocessing and the model building to the validation.

scholarly journals Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fangwan Huang ◽  
Xiuyu Leng ◽  
Mohan Vamsi Kasukurthi ◽  
Yulong Huang ◽  
Dongqi Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aerobic Exercise ◽  
Exercise Testing ◽  
Exercise Intervention ◽  
Cardiopulmonary Exercise Testing ◽  
Machine Learning Techniques ◽  
Hypertensive Patients ◽  
Warm Up ◽  
Cardiopulmonary Exercise ◽  
The Individual

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians’ decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients’ cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected “breath by breath” by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients.

scholarly journals Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 382 ◽  
Author(s):  
Marcos Ruiz-Álvarez ◽  
Francisco Alonso-Sarria ◽  
Francisco Gomariz-Castillo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Air Temperature ◽  
Cross Validation ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using two different approaches, a leave-one-out cross validation in the spatial domain and a spatio-temporal k-block cross-validation, and four different statistics on a daily basis, allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest produces the best results (R 2 = 0.888 ± 0.026, Root mean square error = 3.01 ± 0.325 using k-block cross-validation). Regression methods (Support Vector Machine, Random Forest and Multiple Linear Regression) are calibrated with MODIS data and several predictors easily calculated from a Digital Elevation Model. The most important variables in the Random Forest model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

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