Grey relational grades and neural networks : empirical evidence on vice funds

This research examines time-series predictability of Vice Funds Indices through the Grey Relational Analysis (GRA), and also applies three types of Artificial Neural Networks (ANN) model, namely, Back- propagation Perception Network (BPN), Recurrent Neural Network (RNN), and Radial Basis Function Neural Network (RBFNN) to capture nonlinear tendencies of Vice Funds indices. The study finds that among the three ANN models, BPN has the best predicting power. When the data is separated into 10%, 33% and 50% testing data sets to test the proficiency of the available forecasting information in the time- series of the predictors, the predictive power of the BPN model again dominated the findings 60% of the time. Traders, investors and fund manager can rely on BPN predicting power with large or even small data set. Nevertheless, the result also suggests the predicting power of both RNN and RBFNN model with smaller data sets. Overall, it is suggested that traders and fund managers have stronger chance of achieving more accurate forecasting using the BPN model in Vice Funds indices. Findings of this research have policy implications in the creation of forecasting and investing strategies by examining models that minimize errors in predicting Vice Funds indices.Cette recherche examine la prévisibilité des séries chronologiques des indices Vice Funds par le biais de l'analyse relationnelle grise (GRA), et applique également trois types de modèle de réseaux de neurones artificiels (ANN), à savoir le réseau de perception à propagation arrière (BPN), le réseau de neurones récurrents (RNN) ) et le Radial Basis Function Neural Network (RBFNN) pour capter les tendances non linéaires des indices Vice Funds. L'étude révèle que parmi les trois modèles ANN, BPN a le meilleur pouvoir de prédiction. Lorsque les données sont séparées en 10%, 33% et 50% de jeux de données pour tester la compétence des informations de prévision disponibles dans la série chronologique des prédicteurs, le pouvoir prédictif du modèle BPN de nouveau domine les résultats 60% du temps. Les traders, les investisseurs et le gestionnaire de fonds peuvent compter sur la puissance de prédiction de BPN avec des ensembles de données volumineux ou même petits. Néanmoins, le résultat suggère également la puissance de prédiction des modèles RNN et RBFNN avec des ensembles de données plus petits. Dans l'ensemble, il est suggéré que les traders et les gestionnaires de fonds ont plus de chances d'obtenir des prévisions plus précises en utilisant le modèle BPN dans les indices Vice Funds. Les résultats de cette recherche ont des implications politiques dans la création de stratégies de prévision et d'investissement en examinant des modèles qui minimisent les erreurs de prédiction des indices Vice Funds

Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers to take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs, and it is still an open question. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed. First, we reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we reconstructed the adjacency tensor via Tucker decomposition, wherein more informative and global spatial-temporal dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning are assembled to aggregate and learn the comprehensive spatial-temporal dependencies of the road network. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of the prediction performance and computational cost.Comment: 11 pages, 8 figure

Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model is an efficient tool in stock price prediction which can be applied in the different financial markets, especially the stock market

A Deep Learning Approach for Dynamic Balance Sheet Stress Testing

In the aftermath of the financial crisis, supervisory authorities have considerably improved their approaches in performing financial stress testing. However, they have received significant criticism by the market participants due to the methodological assumptions and simplifications employed, which are considered as not accurately reflecting real conditions. First and foremost, current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models, making their integration a really challenging task with significant estimation errors. Secondly, they still suffer from not employing advanced statistical techniques, like machine learning, which capture better the nonlinear nature of adverse shocks. Finally, the static balance sheet assumption, that is often employed, implies that the management of a bank passively monitors the realization of the adverse scenario, but does nothing to mitigate its impact. To address the above mentioned criticism, we introduce in this study a novel approach utilizing deep learning approach for dynamic balance sheet stress testing. Experimental results give strong evidence that deep learning applied in big financial/supervisory datasets create a state of the art paradigm, which is capable of simulating real world scenarios in a more efficient way.Comment: Preprint submitted to Journal of Forecastin

Discovering Predictable Latent Factors for Time Series Forecasting

Modern time series forecasting methods, such as Transformer and its variants, have shown strong ability in sequential data modeling. To achieve high performance, they usually rely on redundant or unexplainable structures to model complex relations between variables and tune the parameters with large-scale data. Many real-world data mining tasks, however, lack sufficient variables for relation reasoning, and therefore these methods may not properly handle such forecasting problems. With insufficient data, time series appear to be affected by many exogenous variables, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this paper, we develop a novel algorithmic framework for inferring the intrinsic latent factors implied by the observable time series. The inferred factors are used to form multiple independent and predictable signal components that enable not only sparse relation reasoning for long-term efficiency but also reconstructing the future temporal data for accurate prediction. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics via the powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting. The statistical analysis validates the predictability of the learned latent factors

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Predicting U.S. business cycles with recurrent neural networks : An extensive multivariate time-series analysis for comparing LSTM and GRU networks

This study examines how 22 different long short-term memory (LSTM) and gated recurrent unit (GRU) network architectures suit predicting U.S. business cycles. The networks create 91-day forecasts for the dependent variable by using multivariate time-series data comprising 26 leading indicators’ values for the previous 400 days. The proposed models are evaluated by using a train-test split, where the proposed models are trained with data from 1980 to 2005, and the out-of-sample set consists of data between 2005 and 2015. The performance is evaluated by using mean squared error (MSE) and mean absolute error (MAE), and early warning signs are also considered beneficial. The training algorithm consists of typical deep learning methods. MSE and L1 regularization are used for determining the cost, and minibatches of 32 examples are applied together with Nesterov accelerated momentum (NAG) learning algorithm. Early stopping is introduced to halt the training process when strong signs of overfitting are detected. Each proposed recurrent neural network (RNN) architecture is trained three times, and these three networks’ averaged predictions are examined when comparing the architectures. Performance-wise, a few LSTM networks stand out from the other proposed networks. Although the performance results favor the proposed LSTM networks slightly over their GRU equivalents, the difference is not substantial and, in turn, the proposed GRU networks offer less deviation in MSE and MAE between each architecture. However, these steadier performance results do not generate less volatile forecasts. Instead, the best performing networks and architectures differentiate by offering less volatile predictions that also vary less from the real values. Most of the models generate a considerable amount of early warning signs before the 2007 recession, which indicates their suitability for detecting turning points in business cycles. Moreover, a wide range of the proposed LSTM and GRU network architectures learn the general pattern, also the smaller architectures comprising only one hidden layer and less than 500 optimizable parameters. This suggests that these methods offer noteworthy solutions for business cycle forecasting and, more widely, supports applying nonlinear machine learning methods with multivariate data for macroeconomic forecasting tasks where prevalent methods have been found unable to deliver adequate accuracy.Tässä tutkielmassa vertaillaan 22 eri LSTM- ja GRU-neuroverkon soveltuvuutta Yhdysvaltojen taloussyklien ennustamiseen. Valittujen neuroverkkojen tehtävä on luoda 91 päivän ennusteita valitulle selitettävälle muuttujalle käyttämällä 400:n aikaisemman päivän havaintoarvoja 26:sta indikaattorista. Valittujen mallien optimoimiseen käytetään havaintoja ajanjaksolta 1980-2005 ja niiden arviointiin ajanjaksoa 2005-2015. Suorituskyvyn arvioimisessa sovelletaan keskineliövirhettä ja keskiabsoluuttistavirhettä. Tämän lisäksi aikaiset signaalit syklin kääntymisestä nähdään suotuisina. Neuroverkkojen parametrien optimoimiseen käytetty algoritmi sisältää tyypillisiä syväoppimisen menetelmiä. Kustannus määritetään käyttämällä keskineliövirhettä ja L1-termiä. NAG-algoritmia käytetään parametriarvojen päivittämiseen, jolle harjoitus instanssit syötetään 32 kappaleen erissä. Optimoiminen keskeytetään ennen takarajaa, mikäli saadaan merkittäviä viitteitä optimoitavan mallin ylisovittumisesta. Jokainen valittu neuroverkkoarkkitehtuuri treenataan kolme kertaa ja näiden kolmen neuroverkon tuottamien ennusteiden keskiarvoja käytetään pohjana eri arkkitehtuurien vertailussa. Suorituskykyä tarkasteltaessa, muutama LSTM-neuroverkko pystyy saavuttamaan muita vaihtoehtoja paremman tarkkuuden. Vaikka suorituskyvystä kertovat tulokset suosivat valittuja LSTM-arkkitehtuureita, erot LSTM- ja GRU-neuroverkkojen suorituskyvyssä ovat keskimäärin pieniä. Toisaalta, GRU-menetelmät pystyvät tarjoamaan vähemmän vaihtelua arkkitehtuurien keskinäisten neuroverkkojen suorituskyvyssä, mutta tämä ei kuitenkaan johda vakaampiin ennusteisiin. Sen sijaan, parhaat suorituskyvyt antavat LSTM-neuroverkot erottautuvat muista tarjoamalla muita vakaampia ennusteita, jotka myös eroavat todellisista arvoista muita vähemmän. Suurin osa tutkituista malleista tuottaa huomattavan määrän signaaleita syklin vaihtumisesta ennen vuonna 2007 alkanutta lamaa. Sekä pienet että suuret neuroverkot selviävät syklin ennustamisesta pääpiirteissään hyvin, minkä takia LSTM- ja GRU-neuroverkkoja voidaan pitää varteenotettavina vaihtoehtoina taloussyklien ennustamisessa. Tämän lisäksi, tulokset kannustavat soveltamaan epälineaarisia koneoppimismenetelmiä yhdessä usean muuttujan aikasarja-aineistojen kanssa sellaisiin makrotalouden ennusteongelmiin, joihin ei aikaisemmin ole löydetty tarpeellista tarkkuutta saavuttavaa ratkaisua

Ensemble Reinforcement Learning: A Survey

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain challenging to be addressed solely with a single model and algorithm. In response, ensemble reinforcement learning (ERL), a promising approach that combines the benefits of both RL and ensemble learning (EL), has gained widespread popularity. ERL leverages multiple models or training algorithms to comprehensively explore the problem space and possesses strong generalization capabilities. In this study, we present a comprehensive survey on ERL to provide readers with an overview of recent advances and challenges in the field. First, we introduce the background and motivation for ERL. Second, we analyze in detail the strategies that have been successfully applied in ERL, including model averaging, model selection, and model combination. Subsequently, we summarize the datasets and analyze algorithms used in relevant studies. Finally, we outline several open questions and discuss future research directions of ERL. By providing a guide for future scientific research and engineering applications, this survey contributes to the advancement of ERL.Comment: 42 page

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining