Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework.

A novel multi-scale approach for quantifying both inter- and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the data-driven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems

Modeling Energy Consumption of High-Performance Applications on Heterogeneous Computing Platforms

Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit. Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship between workload and power is less understood. Thus, investigation and analysis of power measurements has been a focal point in this work with the aim to improve the general understanding of energy consumption in the context of HPC. This dissertation investigates power and energy consumption of many different parallel applications on several hardware platforms while varying a number of execution characteristics. Multicore and manycore hardware devices are investigated in homogeneous and heterogeneous computing environments. Further, common techniques for reducing power and energy consumption are employed to each of these devices. Well-known power and performance models have been combined to form the Execution-Phase model, which may be used to quantify energy contributions based on execution phase and has been used to predict energy consumption to within 10%. However, due to limitations in the measurement procedure, a less intrusive approach is required. The Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform analysis technique has been applied in innovative ways to model, analyze, and visualize power and energy measurements. EMD is widely used in other research areas, including earthquake, brain-wave, speech recognition, and sea-level rise analysis and this is the first it has been applied to power traces to analyze the complex interactions occurring within HPC systems. Probability distributions may be used to represent power and energy traces, thereby providing an alternative means of predicting energy consumption while retaining the fact that power is not constant over time. Further, these distributions may be used to define the cost of a workload for a given computing platform

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Forecasting tourism demand with an improved mixed data sampling model

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalised dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperform the former

Contribution to Financial Modeling and Financial Forecasting

This thesis consists of three chapters. Each chapter is independent research that is conducted during my study. This research is concentrated on financial time series modeling and forecasting. On first chapter, the research aims to prove that any abnormal behavior in debt level is a signal of future unexpected return for firms that is listed in indexes in this study, hence it is a signal to buy. In order to prove this theory multiple indexes from around the world were taken into consideration. This behavior is consistent in most of indexes around the word. The second chapter investigate the effect of United State president speech on value of United State Currency in Foreign Exchange Rate market. In this analysis it is shown that during the time the president is delivering a speech there is distinctive changes in USD value and volatility in global markets. This chapter implies that this effect cannot be captured by linear models, and the impact of the presidential speech is short term. Finally, the third chapter which is the major research of this thesis, suggest two new methods that potentially enhance the financial time series forecasting. Firstly, the new ARMA-RNN model is presented. The suggested model is inheriting the process of Autoregressive Moving Average model which is extensively studied, and train a recurrent neural network based on it to benefit from unique ability of ARMA model as well as strength and nonlinearity of artificial neural network. Secondly the research investigates the use of different frequency of data for input layer to predict the same data on output layer. In other words, artificial neural networks are trained on higher frequency data to predict lower frequency. Finally, both stated method is combined to achieve more superior predictive model

Wind Power Integration into Power Systems: Stability and Control Aspects

Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting

A Development of a Game-Theoretic Artificially Intelligent Neural Network Revenue Management Forecasting Model

The aim of this dissertation is to create and test a risk induced game-theoretic price forecasting model. The models were tested with datasets from 3 Upper Midscale hotels in 3 locations (urban, interstate and suburb), one hotel from each location. The data was obtained from STR, a leading hospitality marketing company which consolidates all of the daily hotel data from hotels in the United States. Multiple error measures were used to compare the accuracy of models. Three LSTM models were proposed and tested; LSTM model 1 that relied on ADR to forecast ADR, LSTM model 2 that used ADR, supply, demand, and day of the week to generate the forecast, and finally LSTM model 3 that used all the predictors of LSTM model 2 plus ADR of 4 competitors of the same size and scale to predict ADR values. The LSTM models were tested against traditional forecasting methods. The findings showed that LSTM model 2 was the most accurate of all the models tested. Moreover, LSTM model 1 and 3 showed higher accuracy than traditional models in some cases. In particular, all the LSTM models outperformed the traditional methods in the most volatile property (property C). Overall, the results indicated the higher accuracy of LSTM models for times of uncertainty. Finally, estimation of Value at Risk was introduced into the LSTM models, however the accuracy of the models did not change significantly

Symmetry in Structural Health Monitoring

In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment

Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development

The protection and maintenance of environmental resources for future generations require responsible interaction between humans and the environment in order to avoid wasting natural resources. According to an ancient Native American proverb, “We do not inherit the Earth from our ancestors; we borrow it from our children.” This indigenous wisdom has the potential to play a significant role in defining environmental sustainability. Recent technological advances could sustain humankind and allow for comfortable living. However, not all of these advancements have the potential to protect the environment for future generations. Developing societies and maintaining the sustainability of the ecosystem require appropriate wisdom, technology, and management collaboration. This book is a collection of 19 important articles (15 research articles, 3 review papers, and 1 editorial) that were published in the Special Issue of the journal Sustainability entitled “Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development” during 2021-2022.addresses the policymakers and decision-makers who are willing to develop societies that practice environmental sustainability, by collecting the most recent contributions on the appropriate wisdom, technology, and management regarding the different aspects of a community that can retain environmental sustainability