A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN-S model with the implementation of various machine learning algorithms is proposed. This refined BN-S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN-S model. The procedure and the model show the application of data science for extracting a "deterministic component" out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.

This work considers the problem of modified portmanteau tests for testing the adequacy of FARIMA models under the assumption that the errors are uncorrelated but not necessarily independent (i.e. weak FARIMA). We first study the joint distribution of the least squares estimator and the noise empirical autocovariances. We then derive the asymp-totic distribution of residual empirical autocovariances and autocorrelations. We deduce the asymptotic distribution of the Ljung-Box (or Box-Pierce) modified portmanteau statistics for weak FARIMA models. We also propose another method based on a self-normalization approach to test the adequacy of FARIMA models. Finally some simulation studies are presented to corroborate our theoretical work. An application to the Standard \& Poor's 500 and Nikkei returns also illustrate the practical relevance of our theoretical results. AMS 2000 subject classifications: Primary 62M10, 62F03, 62F05; secondary 91B84, 62P05.

Machine learning has been used by scientists in the US to reduce unwanted fluctuations in photon beams from a synchrotron light source. The technique does this by stabilizing the synchrotron's electron beam and offers a way around an important barrier to the development of next-generation facilities. The work was done by Simon Leemann and colleagues at the Lawrence Berkeley National Laboratory (LBNL) in California and could allow emerging analysis techniques that require high beam stability – such as X-ray photon correlation spectroscopy (XPCS) – to be implemented on synchrotons. Synchrotron light sources are extremely useful scientific instruments because they deliver bright, high-quality beams of coherent electromagnetic radiation from infrared wavelengths up to soft X-rays. The light is produced by accelerating electrons in a storage ring using powerful magnets – taking advantage of the fact that an accelerated electron emits electromagnetic radiation.

Erratic oil prices in recent years have made economic diversification essential, and AI is an alternate solution. Having made an early start, these states are positioned to become a key player in AI technology. Dividing the Middle East into four main regions, first, the UAE, second, Saudi Arabia, the GCC 4 comprising of Bahrain, Kuwait, Oman and Qatar on third and lastly Egypt, a PWC research expects the Arab states to accrue two percent of the total global benefits in the next ten years. Projected to mark the highest gains, the UAE would get nearly 14 percent on its GDP in 2030 while the kingdom of Saudi Arabia should make over US $135 billion by that time as well, this being nearly 12.4 percent of its GDP. Assigning large budgets for the speedy implementation of AI, these two GCC states have made a major impact.

The novel service is running until the middle of December as a pilot scheme in Milton Keynes, Buckinghamshire, to see if it is a viable business option in the longer term. The automated delivery vehicles are powered by electric batteries, which supports the environmental credentials of the Enriched range of drinks from supplier Plenish. Kara Rosen, founder of Plenish, said: "We are a future-conscious brand which is always looking to innovate. Through our new Enriched range, we have created the milk of the future – so what better way to deliver it than through a robot milk round? "Our brand ethos is all about'healthy you, healthy planet' – and this is the embodiment of that."

Instead, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes. To do so, they used a computationally intensive model called G4MP2. This collection of molecules, however, represented only a small subset of 166 billion larger molecules that scientists wanted to probe for electrolyte candidates. Because using G4MP2 to resolve each of the 166 billion molecules would have required an impossible amount of computing time and power, the research team used a machine learning algorithm to relate the precisely known structures from the smaller data set to much more coarsely modeled structures from the larger data set.

The term Deep Reinforcement Learning is a new cool phrase in the world of Artificial Intelligence and Machine Learning. So, what does this phrase mean, and what is its impact? Deep Reinforcement Learning uses the combined principles of deep learning and reinforcement learning. Deep Learning, as we know, Deep learning is a part of machine learning methods and is based on artificial neural networks. Reinforcement Learning, on the other hand, is an area of machine learning which tells how software agents should take actions to maximize the probability of choosing the best possible path or behavior for a particular situation.

Woodside Petroleum's chief technology officer believes robots with comparable human dexterity skills are still five to 10 years away. Shaun Gregory told the Resources Technology Showcase that the limited dexterity offered by current robotics technologies suggested there was little immediate danger of humans losing physical skills. "We may lose those physical skills (but) when you look at the robots we are using, certainly not in the very near term," Mr Gregory said. While existing robots had a clasp that enabled them to perform some simple tasks, it was "nowhere near as dexterous as a human hand". Woodside has embraced robotics, advanced sensor design and deployment, artificial intelligence, data science and visualisation, even collaborating with NASA, as a means of reducing costs and improving the efficiency and safety of its oil and gas operations.

Are you sure you want to view these Tweets? Learn why emerging #technologies are creating new mobility opportunities to improve the customer experience. How informed are #insurance #customers when it comes to knowing what products are right for them? As #customer expectations #rapidly evolve, travel brands must transform the #innovation #agenda at the same pace, or risk falling far behind.https://lnkd.in/fajXdRm Prioritizing certain specific #interdependences between 5 key roles of a #CEO highlight differences between a good #performer and a #great one..https://lnkd.in/fDTaJ5Y

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the switching cost. In the paper, we investigate the relation for two classic online settings: Online Algorithms (OA) and Online Convex Optimization (OCO). We provide a new theoretical analysis framework, which shows an interesting observation, that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO. Specifically, the switching cost has significant impact on the dynamic regret in the setting of OA. But, it does not have an impact on the dynamic regret in the setting of OCO. Furthermore, we provide a lower bound of regret for the setting of OCO, which is same with the lower bound in the case of no switching cost. It shows that the switching cost does not change the difficulty of online decision making problems in the setting of OCO.