Bibliografías temáticas / Time series dataset

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Literatura académica sobre el tema "Time series dataset"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 11 de febrero de 2022

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Artículos de revistas sobre el tema "Time series dataset"

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Jiang, Hongyan, Dianjun Fang, Klaus Spicher, Feng Cheng, and Boxing Li. "A New Period-Sequential Index Forecasting Algorithm for Time Series Data." Applied Sciences 9, no. 20 (2019): 4386. http://dx.doi.org/10.3390/app9204386.

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A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for t
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Gütschow, Johannes, M. Louise Jeffery, Robert Gieseke, et al. "The PRIMAP-hist national historical emissions time series." Earth System Science Data 8, no. 2 (2016): 571–603. http://dx.doi.org/10.5194/essd-8-571-2016.

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Abstract. To assess the history of greenhouse gas emissions and individual countries' contributions to emissions and climate change, detailed historical data are needed. We combine several published datasets to create a comprehensive set of emissions pathways for each country and Kyoto gas, covering the years 1850 to 2014 with yearly values, for all UNFCCC member states and most non-UNFCCC territories. The sectoral resolution is that of the main IPCC 1996 categories. Additional time series of CO2 are available for energy and industry subsectors. Country-resolved data are combined from differen
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Weng, Xiaoqing, and Junyi Shen. "Detecting outlier samples in multivariate time series dataset." Knowledge-Based Systems 21, no. 8 (2008): 807–12. http://dx.doi.org/10.1016/j.knosys.2008.03.048.

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de la Cal, Enrique A., José R. Villar, Paula M. Vergara, Álvaro Herrero, and Javier Sedano. "Design issues in Time Series dataset balancing algorithms." Neural Computing and Applications 32, no. 5 (2019): 1287–304. http://dx.doi.org/10.1007/s00521-019-04011-4.

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Jiang, Hongyan, Dianjun Fang, and Xinyan Zhang. "An Adaptive Control Combination Forecasting Method for Time Series Data." Mathematical Problems in Engineering 2021 (June 2, 2021): 1–16. http://dx.doi.org/10.1155/2021/5573170.

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According to the individual forecasting methods, an adaptive control combination forecasting (ACCF) method with adaptive weighting coefficients was proposed for short-term prediction of the time series data. The US population dataset, the American electric power dataset, and the vibration signal dataset in a hydraulic test rig were separately tested by using ACCF method, and then, the accuracy analysis of ACCF method was carried out in the study. The results showed that, in contrast to individual methods or combination methods, the proposed ACCF method was adaptive to adopt one or some of pred
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Burger, Schalk, Searle Silverman, and Gary van Vuuren. "Deriving Correlation Matrices for Missing Financial Time-Series Data." International Journal of Economics and Finance 10, no. 10 (2018): 105. http://dx.doi.org/10.5539/ijef.v10n10p105.

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The problem of missing data is prevalent in financial time series, particularly data such as foreign exchange rates and interest rate indices. Reasons for missing data include the clo-sure of financial markets over weekends and holidays and that sometimes, index data do not change between consecutive dates, resulting in stale data (also considered as missing data). Most statistical software packages function best when applied to complete da-tasets. Listwise deletion – a commonly-used approach to deal with missing data, is straightforward to use and implement, but it can exclude large
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Rußwurm, M., C. Pelletier, M. Zollner, S. Lefèvre, and M. Körner. "BREIZHCROPS: A TIME SERIES DATASET FOR CROP TYPE MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1545–51. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1545-2020.

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Abstract. We present BreizhCrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven recently proposed deep neural networks along with a Random Forest baseline. The dataset, model (re-)implementations and pre-trained model weights are available at the associated GitHub repository (https://github.com/dl4sits/breizhcrops) that has been designed with applic
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Onoghojobi, B., and N. P. Olewuezi. "FORECASTING FUZZY LINEAR REGRESSION MODEL USING TIME SERIES DATASET." Advances in Fuzzy Sets and Systems 21, no. 1 (2016): 7–20. http://dx.doi.org/10.17654/fs021010007.

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Almon, Richard R., William Lai, Debra C. DuBois, and William J. Jusko. "Corticosteroid-regulated genes in rat kidney: mining time series array data." American Journal of Physiology-Endocrinology and Metabolism 289, no. 5 (2005): E870—E882. http://dx.doi.org/10.1152/ajpendo.00196.2005.

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Kidney is a major target for adverse effects associated with corticosteroids. A microarray dataset was generated to examine changes in gene expression in rat kidney in response to methylprednisolone. Four control and 48 drug-treated animals were killed at 16 times after drug administration. Kidney RNA was used to query 52 individual Affymetrix chips, generating data for 15,967 different probe sets for each chip. Mining techniques applicable to time series data that identify drug-regulated changes in gene expression were applied. Four sequential filters eliminated probe sets that were not expre
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Di Martino and Sessa. "Seasonal Time Series Forecasting by F1-Fuzzy Transform." Sensors 19, no. 16 (2019): 3611. http://dx.doi.org/10.3390/s19163611.

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We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index da
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Tesis sobre el tema "Time series dataset"

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Biswas, Debashis. "An Algorithm for Mining Adverse-Event Datasets for Detection of Post Safety Concern of a Drug." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_theses/17.

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Signal detection from Adverse Event Reports (AERs) is important for identifying and analysing drug safety concern after a drug has been released into the market. A safety signal is defined as a possible causal relation between an adverse event and a drug. There are a number of safety signal detection algorithms available for detecting drug safety concern. They compare the ratio of observed count to expected count to find instances of disproportionate reportings of an event for a drug or combination of events for a drug. In this thesis, we present an algorithm to mine the AERs to identify drugs
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Abualhamayl, Abdullah Jameel Mr. "APPLY DATA CLUSTERING TO GENE EXPRESSION DATA." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/259.

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Data clustering plays an important role in effective analysis of gene expression. Although DNA microarray technology facilitates expression monitoring, several challenges arise when dealing with gene expression datasets. Some of these challenges are the enormous number of genes, the dimensionality of the data, and the change of data over time. The genetic groups which are biologically interlinked can be identified through clustering. This project aims to clarify the steps to apply clustering analysis of genes involved in a published dataset. The methodology for this project includes the select
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Tihlaříková, Jana. "Statistické vyhodnocení přijímacích zkoušek." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222848.

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This master’s thesis deals with statistical evaluation of the entrance exams at the Faculty of Business and Management of Brno University of Technology, especially the evaluation of the quality of applicants of bachelor study branch “Tax Advisory“. The thesis also includes the forecast of number of applicants, who will apply for the Faculty of Business and Management of Brno University of Technology in the future.
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Matsubara, Yasuko. "Statistical Data Mining for Time-series Datasets." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/157475.

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Lei, Jiahuan. "An extended BIRCH-based clustering algorithm for large time-series datasets." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29858.

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Temporal data analysis and mining has attracted substantial interest due to theproliferation and ubiquity of time series in many fields. Time series clustering isone of the most popular mining methods, and many time series clustering algorithmsprimarily focus on detecting the clusters in a batch fashion that will use alot of memory space and thus limit the scalability and capability for large timeseries.The BIRCH algorithm has been proven to scale well to large datasets,which is characterized by an incrementally clustering data objects using a singlescan. However the Euclidean distance metric
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Oliveira, Guilherme do Nascimento. "Ordered stacks of time series for exploratory analysis of large spatio-temporal datasets." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/130557.

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O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbano
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Zeng, Jianfeng. "Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617109307510099.

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Oluwole, Oluwadamilola. "Weather-sensitive, spatially-disaggregated electricity demand model for Nigeria." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33043.

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The historical underinvestment in power infrastructure and the poor performance of power delivery has resulted in extensive and regular power shortages in Nigeria. As Nigeria aims to bridge its power supply gap, the recent deregulation of its electricity market has seen the privatisation of its generation and distribution companies. Ambitious plans have also been put in place to expand the transmission network and the total power generation capacity. However, these plans have been developed with essentially arbitrary estimates for prevailing demand levels as the network and generation limits m
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Klinkert, Rickard. "Uncertainty Analysis of Long Term Correction Methods for Annual Average Winds." Thesis, Umeå universitet, Institutionen för fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-59690.

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For the construction of a wind farm, one needs to assess the wind resources of the considered site location. Using reference time series from numerical weather prediction models, global assimilation databases or observations close to the area considered, the on-site measured wind speeds and wind directions are corrected in order to represent the actual long-term wind conditions. This long-term correction (LTC) is in the typical case performed by making use of the linear regression within the Measure-Correlate-Predict (MCP) method. This method and two other methods, Sector-Bin (SB) and Syntheti
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Iwata, Curtis. "A representation method for large and complex engineering design datasets with sequential outputs." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50266.

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This research addresses the problem of creating surrogate models of high-level operations and sustainment (O&S) simulations with time sequential (TS) outputs. O&S is a continuous process of using and maintaining assets such as a fleet of aircraft, and the infrastructure to support this process is the O&S system. To track the performance of the O&S system, metrics such as operational availability are recorded and reported as a time history. Modeling and simulation (M&S) is often used as a preliminary tool to study the impact of implementing changes to O&S systems such as investing in new techno
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Libros sobre el tema "Time series dataset"

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Francois, Joseph F., and Hugo Rojas-Romagosa. The Construction And Interpretation Of Combined Cross-Section And Time-Series Inequality Datasets. The World Bank, 2005. http://dx.doi.org/10.1596/1813-9450-3748.

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Delsol, Laurent. Nonparametric Methods for α-Mixing Functional Random Variables. Редактори Frédéric Ferraty та Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.5.

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This article considers how functional kernel methods can be used to study α-mixing datasets. It first provides an overview of how prediction problems involving dependent functional datasets may arise from the study of time series, focusing on the standard discretized model and modelization that takes into account the functional nature of the evolution of the quantity to be studied over time. It then considers strong mixing conditions, with emphasis on the notion of α-mixing coefficients and α-mixing variables introduced by Rosenblatt (1956). It also describes some conditions for a Markov chain
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Ogorzalek, Thomas K. The Cities on the Hill. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190668877.001.0001.

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Recent electoral cycles have drawn attention to an urban–rural divide at the heart of American politics. This book traces the origins of red and blue America. The urbanicity divide began with the creation of an urban political order that united leaders from major cities and changed the Democratic Party during the New Deal era. These cities, despite being the site of serious, complex conflicts at home, are remarkably cohesive in national politics because members of city delegations represent their city as well as their district. Even though their constituents often don’t see eye-to-eye on impor
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Capítulos de libros sobre el tema "Time series dataset"

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García-López, Daniel-Alejandro, and Héctor-Gabriel Acosta-Mesa. "Discretization of Time Series Dataset with a Genetic Search." In MICAI 2009: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05258-3_18.

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Lee, Garam, Hyunjin Lee, and Kyung-Ah Sohn. "Generating Time Series Simulation Dataset Derived from Dynamic Time-Varying Bayesian Network." In Information Science and Applications 2017. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4154-9_7.

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Scholl, Philipp M., Benjamin Völker, Bernd Becker, and Kristof Van Laerhoven. "A Multi-media Exchange Format for Time-Series Dataset Curation." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_8.

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Ruengronghirunya, Pongsakorn, Vit Niennattrakul, and Chotirat Ann Ratanamahatana. "Speeding Up Similarity Search on a Large Time Series Dataset under Time Warping Distance." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_104.

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Abu Bakar, Azuraliza, Almahdi Mohammed Ahmed, and Abdul Razak Hamdan. "Discretization of Time Series Dataset Using Relative Frequency and K-Nearest Neighbor Approach." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_18.

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Chakraborty, Arijit, Sajal Mitra, Dipankar Das, Debashis De, and Anindya J. Pal. "Forecasting COVID-19 Outbreak in India Using Time Series Dataset: An Ensemble of ARIMA, Abbasov-Mamedova, and Multilayer Perceptron Models." In Advanced Techniques for IoT Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4435-1_17.

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Graniero, Paolo, and Marco Gärtler. "Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_6.

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AbstractBatch runs corresponding to the same recipe usually have different duration. The data collected by the sensors that equip batch production lines reflects this fact: time series with different lengths and unsynchronized events. Dynamic Time Warping (DTW) is an algorithm successfully used, in batch monitoring too, to synchronize and map to a standard time axis two series, an action called alignment. The online alignment of running batches, although interesting, gives no information on the remaining time frame of the batch, such as its total runtime, or time-to-end. We notice that this problem is similar to the one addressed by Survival Analysis (SA), a statistical technique of standard use in clinical studies to model time-to-event data. Machine Learning (ML) algorithms adapted to survival data exist, with increased predictive performance with respect to classical formulations. We apply a SA-ML-based system to the problem of predicting the time-to-end of a running batch, and show a new application of DTW. The information returned by openended DTW can be used to select relevant data samples for the SA-ML system, without negatively affecting the predictive performance and decreasing the computational cost with respect to the same SA-ML system that uses all the data available. We tested the system on a real-world dataset coming from a chemical plant.
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Spiegel, Stephan, David Schultz, and Sahin Albayrak. "BestTime: Finding Representatives in Time Series Datasets." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44845-8_39.

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Tavazzi, Erica, Camille L. Gerard, Olivier Michielin, Alexandre Wicky, Roberto Gatta, and Michel A. Cuendet. "A Process Mining Approach to Statistical Analysis: Application to a Real-World Advanced Melanoma Dataset." In Lecture Notes in Business Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72693-5_22.

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AbstractThanks to its ability to offer a time-oriented perspective on the clinical events that define the patient’s path of care, Process Mining (PM) is assuming an emerging role in clinical data analytics. PM’s ability to exploit time-series data and to build processes without any a priori knowledge suggests interesting synergies with the most common statistical analyses in healthcare, in particular survival analysis. In this work we demonstrate contributions of our process-oriented approach in analyzing a real-world retrospective dataset of patients treated for advanced melanoma at the Lausanne University Hospital. Addressing the clinical questions raised by our oncologists, we integrated PM in almost all the steps of a common statistical analysis. We show: (1) how PM can be leveraged to improve the quality of the data (data cleaning/pre-processing), (2) how PM can provide efficient data visualizations that support and/or suggest clinical hypotheses, also allowing to check the consistency between real and expected processes (descriptive statistics), and (3) how PM can assist in querying or re-expressing the data in terms of pre-defined reference workflows for testing survival differences among sub-cohorts (statistical inference). We exploit a rich set of PM tools for querying the event logs, inspecting the processes using statistical hypothesis testing, and performing conformance checking analyses to identify patterns in patient clinical paths and study the effects of different treatment sequences in our cohort.
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Schultz, Nick, and Mike Bailey. "Using Extruded Volumes to Visualize Time-Series Datasets." In Expanding the Frontiers of Visual Analytics and Visualization. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2804-5_8.

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Actas de conferencias sobre el tema "Time series dataset"

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Stergiou, Stergios, and Jawahar Jain. "Optimizing BDDs for Time-Series Dataset Manipulation." In Design Automation and Test in Europe. IEEE Conference Publications, 2013. http://dx.doi.org/10.7873/date.2013.212.

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Gupta, Aman, and Yadul Raghav. "Time Series Classification with Meta Learning." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101415.

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Meta-Learning, the ability of learning to learn, helps to train a model to learn very quickly on a variety of learning tasks; adapting to any new environment with a minimal number of examples allows us to speed up the performance and training of the model. It solves the traditional machine learning paradigm problem, where it needed a vast dataset to learn any task to train the model from scratch. Much work has already been done on meta-learning in various learning environments, including reinforcement learning, regression task, classification task with image, and other datasets, but it is yet
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Jehangiri, Ali Imran, Ramin Yahyapour, Philipp Wieder, Edwin Yaqub, and Kuan Lu. "Diagnosing Cloud Performance Anomalies Using Large Time Series Dataset Analysis." In 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). IEEE, 2014. http://dx.doi.org/10.1109/cloud.2014.129.

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Saeedi, Panteha, and Soren Aksel Sorensen. "Mathematical-Based Benchmarking: To Predict ANT Exploration Time-Series Dataset." In 2009 International Conference on Computational Intelligence and Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cise.2009.5366677.

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Scholl, Philipp M., and Kristof Van Laerhoven. "A multi-media exchange format for time-series dataset curation." In UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2016. http://dx.doi.org/10.1145/2968219.2968278.

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Avsar, Hulya, Erik Altermann, Christopher Reining, Fernando Moya Rueda, Gernot A. Fink, and Michael ten Hompel. "Benchmarking Annotation Procedures for Multi-channel Time Series HAR Dataset." In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2021. http://dx.doi.org/10.1109/percomworkshops51409.2021.9431062.

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Yi, Michael, Dawson Ramos, Pradeepkumar Ashok, Taylor Thetford, Spencer Bohlander, and Michael Behounek. "Time-Series Data Augmentation Techniques for Improving Automated Drilling Dysfunction Classifiers." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204063-ms.

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Abstract Detecting drilling dysfunctions from surface data is not always easy as downhole vibrations tend to get damped before they reach surface sensors. Building machine learning models to recognize patterns in the surface data requires vibration signals captured by downhole sensors for training purposes. Such datasets are not widely available and therefore a methodology to expand these datasets is highly desirable. This work explores ways to utilize data augmentation to artificially diversify and increase datasets to build better models. Stick-slip (including full-stick), bit bounce, whirl,
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Reining, Christopher, Fernando Moya Rueda, Friedrich Niemann, Gernot A. Fink, and Michael ten Hompel. "Annotation Performance for multi-channel time series HAR Dataset in Logistics." In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2020. http://dx.doi.org/10.1109/percomworkshops48775.2020.9156170.

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Sahoo, Debashis, Naveksha Sood, Usha Rani, George Abraham, Varun Dutt, and A. D. Dileep. "Comparative Analysis of Multi-Step Time-Series Forecasting for Network Load Dataset." In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020. http://dx.doi.org/10.1109/icccnt49239.2020.9225449.

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Chae, Yeonghun, Do-Heon Jeong, and Taehong Kim. "Experimental study of time series-based dataset selection for effective text classification." In 2017 9th International Conference on Knowledge and Smart Technology (KST). IEEE, 2017. http://dx.doi.org/10.1109/kst.2017.7886133.

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Informes sobre el tema "Time series dataset"

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Gradín, Carlos. WIID Companion (March 2021): integrated and standardized series. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/wtn/2021-5.

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This document is part of a series of technical notes describing the compilation of a new companion database that complements the World Income Inequality Database. It aims at facilitating the analysis of inequality as well as progress in achieving the global goal of reducing inequality within and across countries. This new dataset also includes an annual series reporting the income distribution at the percentile level for all citizens in the world, regardless of where they live, since 1950 to present. A previous note described the selection of income distribution series. Since these series may
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Getman, Dan, Brian Bush, Danny Inman, and Ryan Elmore. Evaluation of Methods for Comparison of Spatiotemporal and Time Series Datasets. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1215011.

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Sabhnani, Maheshkumar, Andrew W. Moore, and Artur W. Dubrawski. T-Cube: A Data Structure for Fast Extraction of Time Series from Large Datasets. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada471457.

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