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

A user-friendly guide to using distance measures to compare time series in ecology

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g., assigning species to individual bird calls); clustering (e.g., clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g., accuracy of model predictions to original time series data); and anomaly detection (e.g., detecting possible catastrophic events from population time series). These common tasks in ecological research all rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored. Here, we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well suited to comparing noisy time series and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series and help us answer new ecological questions

Data Mining Paradigm in the Study of Air Quality

Air pollution is a serious global problem that threatens human life and health, as well as the environment. The most important aspect of a successful air quality management strategy is the measurement analysis, air quality forecasting, and reporting system. A complete insight, an accurate prediction, and a rapid response may provide valuable information for society’s decision-making. The data mining paradigm can assist in the study of air quality by providing a structured work methodology that simplifies data analysis. This study presents a systematic review of the literature from 2014 to 2018 on the use of data mining in the analysis of air pollutant measurements. For this review, a data mining approach to air quality analysis was proposed that was consistent with the 748 articles consulted. The most frequent sources of data have been the measurements of monitoring networks, and other technologies such as remote sensing, low-cost sensors, and social networks which are gaining importance in recent years. Among the topics studied in the literature were the redundancy of the information collected in the monitoring networks, the forecasting of pollutant levels or days of excessive regulation, and the identification of meteorological or land use parameters that have the most substantial impact on air quality. As methods to visualise and present the results, we recovered graphic design, air quality index development, heat mapping, and geographic information systems. We hope that this study will provide anchoring of theoretical-practical development in the field and that it will provide inputs for air quality planning and management.Facultad de Ciencias Exacta

Improving the robustness and reliability of population-based global biodiversity indicators

The current global biodiversity crisis is complicated by a data crisis. Reliable tools are needed to guide scientific research and conservation policy decisions, but the data underlying those tools is incomplete and biased. For example, the Living Planet Index (LPI) tracks the changing status of global vertebrate biodiversity, but gaps, biases and quality issues plague the aggregated data used to calculate trends. Unfortunately, we have little understanding of how reliable biodiversity indicators are. In this thesis I develop a suite of tools to assess and improve the reliability of trends in the LPI and similar indicators. First, I explore distance measures as a flexible toolset for comparing time series and trends. I test distance measures for properties related to time series comparisons and rate their relative sensitivities, then expand the results into a framework for choosing an appropriate distance measure for any time series comparison task in ecology. I use the framework to select an appropriate metric for determining trend accuracy. Second, I construct a model of trend reliability from accuracy measurements of sampled trend replicates calculated from artificially generated time series datasets. I apply the model to the LPI to reveal that the majority of trends need more data to be considered reliable, particularly across the global south, and for reptiles and amphibians everywhere. Finally, I develop a method to account for sampling error and serial correlation in confidence intervals of indicators that use aggregated abundance data from different sources. I show that the new method results in more robust and accurate confidence intervals across a wide range of dataset parameters, without reducing trend accuracy. I also apply the method to the LPI to reveal that the current method used by the LPI results in inaccurate and overly wide confidence intervals

Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios

xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario