An investigation into the recurring patterns of forex time series data

Countless theories have been developed by both researchers and financial analyst in an attempt to explain the fluctuation of forex price. By obtaining an intimate understanding of the forex market, traders will hopefully be able to forecast and react to forex price oscillations on-the-fly towards making a profitable investment. In this paper, an investigation into the underlying theory that there exists repeating patterns within the time series data which forms the basis of technical analysis is conducted. The assumption that certain patterns do develop over time and the forex market does not fluctuate in a random manner is used to establish the fact that history repeats itself in forex trading. The patterns and repetitions unveiled within the forex historical data would be an important element for forex forecasting

A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

[EN] In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating im- portant innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pat- tern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are de- veloped and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.The fourth author of this work was partially supported by MINECO, Project MTM2016-75963-P.Arévalo, R.; García, J.; Guijarro, F.; Peris Manguillot, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications. 81:177-192. https://doi.org/10.1016/j.eswa.2017.03.0281771928

Modeling the natural gas supply chain for sustainable growth policy

Natural gas has been used globally as a transitional fuel for supporting a green-energy-supply strategy, which has been questioned for the intermittence and lack of reliability of renewables. This paper proposes a System Dynamics model for assessing alternative security of supply policy along the natural gas value chain. The model incorporates demand, transport, production and reserves of natural gas variables according to a systemic perspective. It also includes a module for evaluating the effect of natural gas price on the demand and supply levels, respectively. Alternative supply policies are evaluated under different scenarios. The chosen case-study focuses on the Colombian natural gas industry with the purpose of assessing how the impact of public policies affect supply and demand. Particularly, policies consider the allocation of resources along the natural gas supply chain, seeking to promote the development of infrastructure oriented to mitigate the risk of provision shortages

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Advances in Modeling and Management of Urban Water Networks

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with analyses on the transition from surface to pressurized flow, impact of water use reduction on the operation of UDSs, and sediment transport in pressurized pipes. The Special Issue also includes one paper dealing with the hydraulic modeling of an urban river with a complex cross-section

Open Data and Models for Energy and Environment

This Special Issue aims at providing recent advancements on open data and models. Energy and environment are the fields of application.For all the aforementioned reasons, we encourage researchers and professionals to share their original works. Topics of primary interest include, but are not limited to:Open data and models for energy sustainability;Open data science and environment applications;Open science and open governance for Sustainable Development Goals;Key performance indicators of data-aware energy modelling, planning and policy;Energy, water and sustainability database for building, district and regional systems; andBest practices and case studies

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%