Time Series Databases Research Articles

Hesitant fuzzy set based computational method for financial time series forecasting

Non-stochastic hesitation in fuzzy time series forecasting methods occurs due to availability of more than one fuzzification methods of time series data. Recently hesitant fuzzy set has gained attention of the researchers to address issue of aforesaid non-stochastic hesitation. In this research paper, we propose and develop a computational algorithm-based method for financial time series forecasting using hesitant fuzzy set. The proposed method uses hesitant fuzzy logical relations that are constructed using triangular fuzzy sets with equal and unequal intervals. In the proposed forecasting method, hesitant fuzzy logical relations are aggregated using a hesitant fuzzy aggregation operator. Advantages of developed hesitant fuzzy set based forecasting method are that it is easy to implement, can cope with huge time series database and enhances the accuracy in financial time series forecast. To see the performance of proposed method in financial time series forecasting, it is implemented on two financial experimental dataset of TAIFEX and SBI share price at BSE. Rigorous comparison analysis of the proposed forecasting method is done by comparing it with other conventional and computational fuzzy time series forecasting methods in terms of RMSE, AFER. Significance of accuracy enhancement in forecasted outputs is also verified using two-tailed t test.

Implementation of Integration VaaMSN and SEMAR for Wide Coverage Air Quality Monitoring

The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G WiFi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm.

The radius inflation problem in short-period low-mass binaries: a large sample analysis

Due to the recent increase in the availability of photometric time-series databases, the characterisation of low-mass eclipsing binaries for the study of their orbital and physical parameters is now possible in large samples and with good precision. We have identified and photometrically characterised a sample of 230 detached close-orbiting eclipsing binaries with low-mass main-sequence components in the Catalina Sky Survey. These low-mass stars have masses of $M \leq 1.0\ M_{\odot}$ and orbital periods shorter than $2$ days. The adopted method provided a robust estimate of stellar parameters (as mass and fractional radius) by using only light curves and photometric colours, since no spectroscopic information was available for these objects. A SDSS-2MASS ten-colour grid of composite synthetic and observed colours and the K-Nearest Neighbours method were employed to identify main-sequence stars and to estimate their effective temperatures, typically of $T_{\rm eff}\leq 5720$ K. Each light curve was modelled with the JKTEBOP code together with an asexual genetic algorithm to obtain the most coherent values for the fitted parameters. The present work provides an unprecedented number of homogeneous estimates of main stellar parameters in short-period low-mass binary systems. The distribution of the components of the investigated detached eclipsing binaries in the mass-radius diagram supports a trend of radius inflation on low-mass main-sequence stars. A relative increase of inflation for lower masses is also found and our results suggest that the secondaries are more inflated, i.e. they present larger radii than the primary components of same mass, when compared to stellar evolutionary models.

An access and inference control model for time series databases

Today, many applications produce and use time series data. The data of this type may contain sensitive information. So they should be protected against unauthorized accesses. In this paper, security issues of time series data are identified and an access and inference control model for satisfying the identified security requirements is proposed. Using this model, administrators can define authorization rules based on various time-based granularities (e.g. day or month) and apply value-based constraints over the accessed times series data. Furthermore, they can define policy rules over the composition of multiple time-series other than the base time-series data. Detecting and resolving different types of conflicts between the simple, aggregation, or composition access rules over the time series data is a challenging issue which is investigated in this paper. Detecting explicit and implicit conflicts in this model prevents information inference from hierarchical time series data. To prove the applicability of the model, a reference monitor based on the proposed model has been implemented as a secure access layer on top of OpenTSDB (a time series database). Our evaluation shows that the overhead imposed on queries by the secure access layer depends on several parameters such as class of query, number of authorization rules, complexity of value constraints, and number of data being accessed. In our experiments, the overhead was variable in the range of 4% in the best case to 184% in the worst case.

BIM- and IoT-based monitoring framework for building performance management

ABSTRACTDuring the operation and maintenance period of buildings, a monitoring system established to monitor the internal environment can be very helpful to ensure that buildings are in good condition. However, the existing monitoring systems still need improvements in data interoperability and smart sensing. This paper established a new monitoring framework based on Building Information Modeling (BIM) and IoT (Internet of Things) Technology. In this proposed framework, BIM is used to maintain the 3D data of the building, while a time-series database called InfluxDB is introduced for storage of monitoring data. Then, a monitoring server based on Message Queuing Telemetry Transport protocol is established, providing a low-cost and reliable communication. Meanwhile, Arduino microcontrollers with some sensors are adopted as the nodes of the monitoring network, aiming to collect real-time data of the environment and upload the data to the server. Finally, a prototype system is developed to demonstrate the proposed framework. Testing of the developed system and corresponding IoT sensors shows that combination of BIM and IoT technology provides a comprehensive view of the status of the buildings and improves the efficiency of information utilization.

A Wave of Global Sociopolitical Destabilization of the 2010s: A Quantitative Analysis

ABSTRACTAfter the beginning of the Arab Spring in 2011, explosive global growth was observed for the majority of indicators of sociopolitical destabilization in all parts of the World System. In order to identify the structure of this destabilization wave, we apply a series of statistical techniques such as trend analysis and t-tests to study the degrees of intensification of various instability indicators (as recorded by the Cross National Time Series database). We reveal explosive global growth in anti-government demonstrations, riots, general strikes, terrorist attacks/guerrilla warfare, and purges, as well as in the global integral index of sociopolitical destabilization. On the other hand, no statistically significant growth has been detected for assassinations and major government crises, whereas for such an important indicator of global sociopolitical destabilization, as the global number of coups and coup attempts, we find a statistically significant decrease.

C-AQM: A CROWD-SOURCED AIR QUALITY MONITORING SYSTEM

Abstract. European cities are currently facing one of the main evolutions of the last fifty years. “Cities for the citizens” is the new leitmotiv of modern societies, and citizens are demanding, among others, a greener environment including non-polluted air. Improved sensors and improved communication systems open the door to the design of new systems based on citizen science to better monitor the air quality. In this paper, we present a system that relies on the already available Copernicus Environment Service, on Air Quality Monitoring reference stations and on a cluster of new low-cost, low-energy sensor nodes that will improve the resolution of air quality maps. The data collected by this system will be stored in a time series database, and it will be available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main challenges imposed by Air Quality Monitoring systems, our proposal to overcome those challenges, and the results of our preliminary tests.

FluteDB: An efficient and scalable in-memory time series database for sensor-cloud

Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availability are also difficult to be guaranteed. To deal with the problem, this paper proposes FluteDB, an efficient and scalable in-memory time series database for sensor-cloud. We adequately analyze the unique characteristics of time series data and its relevant operations to strike the right balance among efficiency, scalability, resources consumption, reliability and availability. Specifically, on basis of the aggregate analysis of root cause for ongoing time series problems, FluteDB targeted optimizes the strategies for key operations in memory and physical storage, at the expense of partial acceptable data precision and consistency. FluteDB’s enhanced strategies are primarily comprised of Triggered Time Series Merge Tree (TTSM Tree), time series enhanced cache management and corresponding compression algorithms for different data types. The validations of all sub-modules have demonstrated that our improved strategies outperform existing methods in real time series environment significantly. Global experimental results also show that the integrated FluteDB reduces query latency by 17x, improves write rate by 98x and saves about 47% storage resources. The average available service time and recovery rate and degree of FluteDB are competitive with the state-of-the-art reliability and availability strategy in real and simulated faults, which demonstrates FluteDB can provide highly stable large-scale data cloud services.

Predicting adverse hemodynamic events in critically ill patients.

The art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized. Data mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains. Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

Chasing entrepreneurial firms

The search for a reliable data-set of entrepreneurial firms is ongoing. We analyze and assess longitudinal data on startups from two data sources – the National Establishment Time-Series (NETS) database and the Secretary of State (SOS) business registry data. Our primary purposes in this paper are to assess the usefulness and reliability of these databases in measuring startup activity along several quality indicators and to explore the possibility of integrating these large databases using both automated and manual processes. The NETS identifies a firm’s employment, sales, and industry but is expensive and suffers from a temporal lag. The SOS data provide up-to-date startup counts but offer limited variables. We conclude that policymakers and researchers will benefit from combing both the SOS and adjusted NETS since they provide complementary information on startups. We carefully document our methodology and make suggestions for use of the data for future research.

Performance assessment of grid-interactive solar photovoltaic projects under India’s national solar mission

Lack of long-term global solar radiation data has often been a significant challenge to the solar power sector development primarily in developing countries. The choice of a solar radiation database is projected to have a considerable impact on the predicted performance of a solar power project and consequently on its techno-commercial viability. Therefore, use of reliable and well- characterized solar radiation data source is important for bankability of solar power projects. This study presents the technical and economic performance evaluation of grid-interactive solar photovoltaic (PV) projects implemented under the first phase of India’s national solar mission. For performance assessment, we compare annual energy yield predictions using several solar radiation databases and monitored data of 39 solar PV power plants located across the country. Technical simulations have been carried out for each project location using static and dynamic solar irradiance data obtained from various databases available in the Indian context. PVSYST software has been used for energy yield assessment of solar PV projects after taking into account the key design and technical parameters and associated energy losses during solar energy conversion. The inter-comparability of capacity utilization factor and levelized cost of electricity of operational solar PV projects have also been analyzed with the estimates obtained through different solar radiation databases. Mutual deviation for the techno-economic performance of solar PV projects varied from −12% to 31% for the projects under the first phase of India’s solar mission. Our study indicates that the long-term measured or high-resolution time series databases should be preferred for the bankability of solar power projects. Further, solar power policies of the country must provide clear guidelines for selection of solar radiation databases to enhance their bankability.

Are active labour market policies effective in activating and integrating low-skilled individuals? An international comparison

This paper examines the effectiveness of active labour market policies (ALMPs) in improving labour market outcomes, especially of low-skilled individuals, by means of a pooled cross-country and time series database for 31 advanced countries during the period 1985–2010. The analysis includes aspects of the delivery system to see how the performance of ALMPs is affected by different implementation characteristics. Among the notable results, the paper finds that ALMPs matter at the aggregate level, but mostly through an appropriate management and implementation. In this regard, sufficient allocation of resources to programme administration and policy continuity appear to be particularly important. Moreover, start-up incentives and measures aimed at vulnerable populations are more effective than other ALMPs in terms of reducing unemployment and increasing employment. Interestingly, the positive effects of these policies seem to be particularly beneficial for the low skilled.JEL Classification: J08, E24, H5, J68, D78

Energy renovation rates in the Netherlands – comparing long and short term prediction methods

Energy renovation rates in the Netherlands – comparing long and short term prediction methods

An Analysis of Motion Transition in Subtle Errors using Inductive Logic Programming

This article seeks to utilize the data collected from virtual reality (VR)-based software and a leap-motion device used for learning of subtle errors in mild cognitive impairment (MCI) cases to enable early detection of MCI by analyzing the classification rules for errors (action slips) based on finger-action transitions when performing instrumental activities of daily living (IADL). Finger motion was recorded as a time-series database. An induction technique known as Inductive-Logic Programming (ILP), which uses logical and clausal language to represent the training data, was then used to discover a concise classification rule using logical programming. The content within this article was able to generate rules on how action transitions of the finger in the experiments were related to the pattern of micro-errors that indicate the difference of error regarding the length of the no-motion state of the finger.

Improving Read Latency for Stream Data Processing via Parallel Access of Time Series Database

Improving Read Latency for Stream Data Processing via Parallel Access of Time Series Database

Using Empirical Recurrence Rates Ratio for Time Series Data Similarity

Several methods exist in classification literature to quantify the similarity between two time series data sets. Applications of these methods range from the traditional Euclidean-type metric to the more advanced Dynamic Time Warping metric. Most of these adequately address structural similarity but fail in meeting goals outside it. For example, a tool that could be excellent to identify the seasonal similarity between two time series vectors might prove inadequate in the presence of outliers. In this paper, we have proposed a unifying measure for binary classification that performed well while embracing several aspects of dissimilarity. This statistic is gaining prominence in various fields, such as geology and finance, and is crucial in time series database formation and clustering studies.

Short-term cloud coverage prediction using the ARIMA time series model

ABSTRACTIn view of the important role of cloud coverage on the solar (energy) irradiance, the total cloud coverage prediction based on ground-based cloud images is studied in this paper. In traditional prediction techniques, the correlation between cloud coverage over continue time is always neglected. Thus, an autoregressive integrated moving average (ARIMA) time series model is used to predict the short-term cloud coverage. Experimental results on a collected time series database of cloud coverage computed from ground-based cloud images show that the correlation information of time series is useful for cloud coverage prediction. Additionally, the ARIMA model gains a superior prediction performance for forecasts of one minute or longer 20 and 30 minutes. We are able to predict the cloud coverage with an approximate error of 5%, 7%, and 9% for 1, 5, and 20 and 30 minute forecasts, respectively. Furthermore, we found that there are different error rates of predictions for different cloud coverage intervals. High cloud coverage always suffers from a higher error rate.

Mining non-redundant closed flexible periodic patterns

Mining periodic patterns from time series databases is needed to predict any future situation. Flexible pattern mining is a special kind of periodic pattern mining where intermediate events can be overlooked purposely. Mining such patterns from time series data is advantageous due to its capability of modeling various real life scenarios. The goal of mining closed flexible patterns is to avoid unnecessary flexible patterns but preserving the same information of a complete set of patterns. Though it has wide range of application domains, existing algorithms failed to mine closed flexible patterns without generating any false positive, i.e. non-closed and ∕or redundant patterns. In this paper, a new algorithm NRCFP (Non-Redundant Closed Flexible Pattern) has been proposed that generates complete set of non-redundant closed flexible patterns in time series databases. Three pruning techniques- BackScan (existing), RangeScan (proposed) and Column-pruning (proposed) have been applied to avoid generation of non-closed patterns, redundant flexible patterns and fictitious patterns. Proposed NRCFP efficiently mines non-redundant closed flexible periodic patterns. The performance of our algorithm has been extensively analyzed using several real-life databases based on runtime and memory consumption and compared with existing state-of-the-arts approach to prove effectiveness of the algorithm with respect to required processing time and memory consumption. Some applications of our proposed algorithm in various real life domains are discussed.

Analysis of Rhythms in Experimental Signals

We compare algorithms designed to extract quasiperiodic components of a signal and estimate the amplitude, phase, stability, and other characteristics of a rhythm in a sliding window in the presence of data gaps. Each algorithm relies on its own rhythm model; therefore, it is necessary to use different algorithms depending on the research objectives. The described set of algorithms and methods is implemented in the WinABD software package, which includes a time-series database management system, a powerful research complex, and an interactive data-visualization environment.

An Assessment of the National Establishment Time Series (NETS) Database

The National Establishment Time Series (NETS) is a private sector source of U.S. business microdata. Researchers have used state-specific NETS extracts for many years, but relatively little is known about the accuracy and representativeness of the nationwide NETS sample. We explore the properties of NETS as compared to official U.S. data on business activity: The Census Bureau's County Business Patterns (CBP) and Nonemployer Statistics (NES) and the Bureau of Labor Statistics' Quarterly Census of Employment and Wages (QCEW). We find that the NETS universe does not cover the entirety of the Census-based employer and nonemployer universes, but given certain restrictions NETS can be made to mimic official employer datasets with reasonable precision. The largest differences between NETS employer data and official sources are among small establishments, where imputation is prevalent in NETS. The most stringent of our proposed sample restrictions still allows scope that cover s about three quarters of U.S. private sector employment. We conclude that NETS microdata can be useful and convenient for studying static business activity in high detail.