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Artículos de revistas 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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1

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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2

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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3

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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4

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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5

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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6

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 &ndash; a commonly-used approach to deal with missing data, is straightforward to use and implement, but it can exclude large

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7

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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8

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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9

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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10

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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11

Chagas, Vinícius B. P., Pedro L. B. Chaffe, Nans Addor, et al. "CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil." Earth System Science Data 12, no. 3 (2020): 2075–96. http://dx.doi.org/10.5194/essd-12-2075-2020.

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Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3679 gauges, as well as meteorological forcing (precipitation, evapotranspiration, and temperature) for 897 selected catchments. It also includes 65 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations, an

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12

Dan, Jingpei, Weiren Shi, Fangyan Dong, and Kaoru Hirota. "Piecewise Trend Approximation: A Ratio-Based Time Series Representation." Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/603629.

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A time series representation, piecewise trend approximation (PTA), is proposed to improve efficiency of time series data mining in high dimensional large databases. PTA represents time series in concise form while retaining main trends in original time series; the dimensionality of original data is therefore reduced, and the key features are maintained. Different from the representations that based on original data space, PTA transforms original data space into the feature space of ratio between any two consecutive data points in original time series, of which sign and magnitude indicate chang

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13

Chae, Yeonghun, and Do-Heon Jeong. "Time-Series based Dataset Selection Method for Effective Text Classification." Journal of the Korea Contents Association 17, no. 1 (2017): 39–49. http://dx.doi.org/10.5392/jkca.2017.17.01.039.

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14

Hubert-Moy, Laurence, Jeanne Thibault, Elodie Fabre, et al. "Time-series spectral dataset for croplands in France (2006–2017)." Data in Brief 27 (December 2019): 104810. http://dx.doi.org/10.1016/j.dib.2019.104810.

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15

Nazir, S., Azlan Ab Aziz, J. Hosen, Nor Azlina Aziz, and G. Ramana Murthy. "Forecast Energy Consumption Time-Series Dataset using Multistep LSTM Models." Journal of Physics: Conference Series 1933, no. 1 (2021): 012054. http://dx.doi.org/10.1088/1742-6596/1933/1/012054.

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Agarwal, Shubham, Christian Muise, Mayank Agarwal, et al. "TraceHub - A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13600–13601. http://dx.doi.org/10.1609/aaai.v34i09.7087.

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In this paper, we present TraceHub - a platform that connects new non-trivial state-of-the-art time-series analytics with datasets from different domains. Analytics owners can run their insights on new datasets in an automated setting to find insight's potential and improve it. Dataset owners can find all possible types of non-trivial insights based on latest research. We provide a plug-n-play system as a set of Dataset, Transformer pipeline, and Analytics APIs for both kinds of users. We show a usefulness measure of generated insights across various types of analytics in the system. We believ

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17

Dobrzyński, Maciej, Marc-Antoine Jacques, and Olivier Pertz. "Mining single-cell time-series datasets with Time Course Inspector." Bioinformatics 36, no. 6 (2019): 1968–69. http://dx.doi.org/10.1093/bioinformatics/btz846.

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Abstract Summary Thanks to recent advances in live cell imaging of biosensors, microscopy experiments can generate thousands of single-cell time-series. To identify sub-populations with distinct temporal behaviours that correspond to different cell fates, we developed Time Course Inspector (TCI)—a unique tool written in R/Shiny to combine time-series analysis with clustering. With TCI it is convenient to inspect time-series, plot different data views and remove outliers. TCI facilitates interactive exploration of various hierarchical clustering and cluster validation methods. We showcase TCI b

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Zhong, Bo, Aixia Yang, Kunsheng Jue, and Junjun Wu. "Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin." Remote Sensing 13, no. 8 (2021): 1596. http://dx.doi.org/10.3390/rs13081596.

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Long time series of land cover changes (LCCs) are critical in the analysis of long-term climate, environmental, and ecological changes. Although several moderate to fine resolution global land cover datasets have been publicly released and they show strong consistency at the global scale, they have large deviations at the regional scale; furthermore, high-quality land cover datasets from before 2000 are not available and the classification consistency among different datasets is not very good. Thus, long time series of land cover datasets with high quality and consistency are in great demand b

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Zhang, Wei, Zhihai Wang, Jidong Yuan, and Shilei Hao. "Shapelet Discovery by Lazy Time Series Classification." Computational Intelligence and Neuroscience 2020 (October 24, 2020): 1–19. http://dx.doi.org/10.1155/2020/1978310.

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As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and

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20

Tan, Yi-Fei, Xiaoning Guo, and Soon-Chang Poh. "Time series activity classification using gated recurrent units." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3551. http://dx.doi.org/10.11591/ijece.v11i4.pp3551-3558.

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<span>The population of elderly is growing and is projected to outnumber the youth in the future. Many researches on elderly assisted living technology were carried out. One of the focus areas is activity monitoring of the elderly. AReM dataset is a time series activity recognition dataset for seven different types of activities, which are bending 1, bending 2, cycling, lying, sitting, standing and walking. In the original paper, the author used a many-to-many Recurrent Neural Network for activity recognition. Here, we introduced a time series classification method where Gated Recurrent

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Kamycki, Krzysztof, Tomasz Kapuscinski, and Mariusz Oszust. "Data Augmentation with Suboptimal Warping for Time-Series Classification." Sensors 20, no. 1 (2019): 98. http://dx.doi.org/10.3390/s20010098.

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In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series data

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Oliveira, Eduardo E., Vera L. Miguéis, Luís Guimarães, and José Borges. "Power Transformer Failure Prediction: Classification in Imbalanced Time Series." U.Porto Journal of Engineering 3, no. 2 (2018): 34–48. http://dx.doi.org/10.24840/2183-6493_003.002_0004.

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This paper describes a study on applying data mining techniques to power transformer failure prediction. The data set used consisted not only on DGA tests, but also in other tests done to the transformer’s insulating oil. This dataset presented several challenges, such as highly imbalanced classes (common in failure prediction problems), and the temporal nature of the observations.To overcome these challenges, several techniques were applied for prediction and better understand the dataset. Pre-processing and temporality incorporation in the dataset is discussed. For prediction, a 1-class and

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23

Zhou, Dongbo, Shuangjian Liu, Jie Yu, and Hao Li. "A High-Resolution Spatial and Time-Series Labeled Unmanned Aerial Vehicle Image Dataset for Middle-Season Rice." ISPRS International Journal of Geo-Information 9, no. 12 (2020): 728. http://dx.doi.org/10.3390/ijgi9120728.

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The existing remote sensing image datasets target the identification of objects, features, or man-made targets but lack the ability to provide the date and spatial information for the same feature in the time-series images. The spatial and temporal information is important for machine learning methods so that networks can be trained to support precision classification, particularly for agricultural applications of specific crops with distinct phenological growth stages. In this paper, we built a high-resolution unmanned aerial vehicle (UAV) image dataset for middle-season rice. We scheduled th

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Borkin, Dmitrii, Martin Németh, German Michaľčonok, and Olga Mezentseva. "Adding Additional Features to Improve Time Series Prediction." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 27, no. 45 (2019): 72–78. http://dx.doi.org/10.2478/rput-2019-0028.

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Abstract This paper aims at the time-series data analysis. We propose the possibility of adding additional features to the existing time series data set, to improve the prediction performance of the prediction model. The main goal of our research was to find a proper method for building a prediction model for the time-series data, using also machine learning methods. In this phase of research, we aim at the data analysis and proposal of the ways to add additional features to our dataset. In this paper, we aim at adding derived parameters from one of the original features. We also propose incor

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Twumasi-Ankrah, Sampson, Simon Kojo Appiah, Doris Arthur, Wilhemina Adoma Pels, Jonathan Kwaku Afriyie, and Danielson Nartey. "Comparison of outlier detection techniques in non-stationary time series data." Global Journal of Pure and Applied Sciences 27, no. 1 (2021): 55–60. http://dx.doi.org/10.4314/gjpas.v27i1.7.

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This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques u

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Casado-Vara, Roberto, Angel Martin del Rey, Daniel Pérez-Palau, Luis de-la-Fuente-Valentín, and Juan M. Corchado. "Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training." Mathematics 9, no. 4 (2021): 421. http://dx.doi.org/10.3390/math9040421.

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Evaluating web traffic on a web server is highly critical for web service providers since, without a proper demand forecast, customers could have lengthy waiting times and abandon that website. However, this is a challenging task since it requires making reliable predictions based on the arbitrary nature of human behavior. We introduce an architecture that collects source data and in a supervised way performs the forecasting of the time series of the page views. Based on the Wikipedia page views dataset proposed in a competition by Kaggle in 2017, we created an updated version of it for the ye

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Domonkos, P. "Homogenising time series: beliefs, dogmas and facts." Advances in Science and Research 6, no. 1 (2011): 167–72. http://dx.doi.org/10.5194/asr-6-167-2011.

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Abstract. In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation

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Kafkes, Diana, and Jason St. John. "BOOSTR: A Dataset for Accelerator Control Systems." Data 6, no. 4 (2021): 42. http://dx.doi.org/10.3390/data6040042.

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The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We ar

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Bennett, D. P., R. J. Cuss, P. J. Vardon, J. F. Harrington, R. N. Philp, and H. R. Thomas. "Data analysis toolkit for long-term, large-scale experiments." Mineralogical Magazine 76, no. 8 (2012): 3355–64. http://dx.doi.org/10.1180/minmag.2012.076.8.48.

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AbstractA new data analysis toolkit which is suitable for the analysis of large-scale, long-term datasets and the phenomenon/anomalies they represent is described. The toolkit aims to expose and quantify scientific information in a number of forms contained within a time-series based dataset in a quantitative and rigorous manner, reducing the subjectivity of observations made, thereby supporting the scientific observer. The features contained within the toolkit include the ability to handle non-uniform datasets, time-series component determination, frequency component determination, feature/ev

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YUAN, JI-DONG, ZHI-HAI WANG, and MENG HAN. "A DISCRIMINATIVE SHAPELETS TRANSFORMATION FOR TIME SERIES CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 06 (2014): 1450014. http://dx.doi.org/10.1142/s0218001414500141.

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Time series shapelets are subsequences of time series that could be representative of a class. Shapelets-based time series classification methods can be divided into two large categories. The first category integrates shapelets selection within the process of constructing classifier; while the second category disconnects the process of finding shapelets from the classification algorithm by adopting a shapelet transformation. However, there are two important limitations of shapelet transformation. First, the number of shapelets selected for transformation has great influence on classification r

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Wan, Yuqing, Raymond Yiu Keung Lau, and Yain-Whar Si. "Mining subsequent trend patterns from financial time series." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 03 (2020): 2050010. http://dx.doi.org/10.1142/s0219691320500101.

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Chart patterns are one of the important tools used by the financial analysts for predicting future price trends (subsequent trends) in stock markets. Although many works related to the descriptions of chart patterns and several effective methods to identify chart patterns from the financial time series have been proposed in recent years, there is no in-depth study about the general characteristics of the subsequent trends. In this paper, we proposed a general framework for mining subsequent trend for chart patterns. We extensively analyze the characteristics of subsequent trends of chart patte

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Julien, Y., and J. A. Sobrino. "TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods." Revista de Teledetección, no. 51 (June 29, 2018): 19. http://dx.doi.org/10.4995/raet.2018.9749.

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<p>This paper introduces the Time Series Simulation for Benchmarking of Reconstruction Techniques (TISSBERT) dataset, intended to provide a benchmark for the validation and comparison of time series reconstruction methods. Such methods are routinely used to estimate vegetation characteristics from optical remotely sensed data, where the presence of clouds decreases the usefulness of the data. As for their validation, these methods have been compared with previously published ones, although with different approaches, which sometimes lead to contradictory results. We designed the TISSBERT

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Azura Md Ghani, Nor, Saadi Bin Ahmad Kamaruddin, Ismail Musirin, and Hishamuddin Hashim. "Fitting Conventional Neural Network Time Series Models on Sand Price Indices Dataset." International Journal of Engineering & Technology 7, no. 3.15 (2018): 11. http://dx.doi.org/10.14419/ijet.v7i3.15.17396.

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This result-based paper discusses on the best aftereffects of both fitted BPNN-NAR and BPNN-NARMA on MCCI Sand dataset regarding distinctive error measures. This exploration examine the outcomes as far as the execution of the fitted forecasting models by every arrangement of input lags and error lags utilized, the execution of the fitted anticipating models by various hidden nodes utilized, the execution of the fitted estimating models when joining both inputs and hidden nodes, the consistency of error measures utilized for the fitted determining models, and in addition the general best fitted

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Olsavszky, Victor, Mihnea Dosius, Cristian Vladescu, and Johannes Benecke. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database." International Journal of Environmental Research and Public Health 17, no. 14 (2020): 4979. http://dx.doi.org/10.3390/ijerph17144979.

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The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most

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Zhang, Hanbo, Peng Wang, Zicheng Fang, Zeyu Wang, and Wei Wang. "ELIS++: a shapelet learning approach for accurate and efficient time series classification." World Wide Web 24, no. 2 (2021): 511–39. http://dx.doi.org/10.1007/s11280-020-00856-1.

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AbstractIn recent years, time series classification with shapelets, due to the high accuracy and good interpretability, has attracted considerable interests. These approaches extract or learn shapelets from the training time series. Although they can achieve higher accuracy than other approaches, there still confront some challenges. First, they may suffer from low accuracy in the case of small training dataset. Second, they must manually set some parameters, like the number of shapelets and the length of each shapelet beforehand, and some hyper-parameters, like learning rate and regulation we

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Pereira, Lucas, Vitor Aguiar, and Fábio Vasconcelos. "FIKWaste: A Waste Generation Dataset from Three Restaurant Kitchens in Portugal." Data 6, no. 3 (2021): 25. http://dx.doi.org/10.3390/data6030025.

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In the era of big data and artificial intelligence, public datasets are becoming increasingly important for researchers to build and evaluate their models. This paper presents the FIKWaste dataset, which contains time series data for the volume of waste produced in three restaurant kitchens in Portugal. Organic (undifferentiated) and inorganic (glass, paper, and plastic) waste bins were monitored for a consecutive period of four weeks. In addition to the time series measurements, the FIKWaste dataset contains labels for waste disposal events, i.e., when the waste bins are emptied, and technica

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Xiao, Zhiwen, and Jianbin Jiao. "Explainable Fraud Detection for Few Labeled Time Series Data." Security and Communication Networks 2021 (June 12, 2021): 1–9. http://dx.doi.org/10.1155/2021/9941464.

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Fraud detection technology is an important method to ensure financial security. It is necessary to develop explainable fraud detection methods to express significant causality for participants in the transaction. The main contribution of our work is to propose an explainable classification method in the framework of multiple instance learning (MIL), which incorporates the AP clustering method in the self-training LSTM model to obtain a clear explanation. Based on a real-world dataset and a simulated dataset, we conducted two comparative studies to evaluate the effectiveness of the proposed met

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WEISENT, J., W. SEAVER, A. ODOI, and B. ROHRBACH. "Comparison of three time-series models for predicting campylobacteriosis risk." Epidemiology and Infection 138, no. 6 (2010): 898–906. http://dx.doi.org/10.1017/s0950268810000154.

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SUMMARYThree time-series models (regression, decomposition, and Box–Jenkins autoregressive integrated moving averages) were applied to national surveillance data for campylobacteriosis with the goal of disease forecasting in three US states. Datasets spanned 1998–2007 for Minnesota and Oregon, and 1999–2007 for Georgia. Year 2008 was used to validate model results. Mean absolute percent error, mean square error and coefficient of determination (R2) were the main evaluation fit statistics. Results showed that decomposition best captured the temporal patterns in disease risk. Training dataset R2

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Huang, Chang, Yun Chen, Shiqiang Zhang, Linyi Li, Junfeng Shui, and Qihang Liu. "Integrating Water Observation from Space Product and Time-Series Flow Data for Modeling Spatio-Temporal Flood Inundation Dynamics." Remote Sensing 11, no. 21 (2019): 2535. http://dx.doi.org/10.3390/rs11212535.

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Periodic inundation of floodplains and wetlands is critical for the well being of ecosystems. This study proposes a simple but efficient model that integrates time series daily flow data and the Landsat-derived Water Observation from Space (WOfS) product to model the spatio-temporal flood inundation dynamics of the Murray-Darling Basin. A zone-gauge framework is adopted in order to reduce the hydrologic complexity of the large river basin. Under this framework, flood frequency analysis was conducted at each gauge station to identify historical peak flows and their annual exceedance probabiliti

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40

Gružauskas, Valentas, Dalia Čalnerytė, Tautvydas Fyleris, and Andrius Kriščiūnas. "Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities." Real Estate Management and Valuation 29, no. 3 (2021): 39–51. http://dx.doi.org/10.2478/remav-2021-0020.

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Abstract The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means cluster

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41

Rivero, Cristian Rodrìguez, Juliàn Pucheta, Sergio Laboret, Vìctor Sauchelli, and Daniel Patiǹo. "Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets." Journal of Artificial Intelligence and Soft Computing Research 7, no. 1 (2017): 5–16. http://dx.doi.org/10.1515/jaiscr-2017-0001.

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Abstract This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is pre

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42

SIPES, TAMARA, NATASHA BALAC, HOMA KARIMABADI, NICOLE WOLTER, KENNETH NUNES, and AARON ROBERTS. "A MULTIVARIATE TIME SERIES CLASSIFICATION METHOD FOR STREAMING DATA USING TEMPORAL METAFEATURE ABSTRACTIONS." International Journal of Semantic Computing 07, no. 02 (2013): 173–83. http://dx.doi.org/10.1142/s1793351x13400084.

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In this paper we demonstrate a new approach to the classification of multivariate time series streaming data by utilizing a temporal metafeature abstractions method. The technique extracts global features and metafeatures in order to capture the necessary time-lapse information in the streams of data. The features are then used to create a static, intermediate stream representation that includes all the important time-varying information, and is suitable for analysis using the standard supervised data mining techniques. The capability of the new algorithm called MineTool-TS2 was demonstrated t

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43

Yang, Jun-He, Ching-Hsue Cheng, and Chia-Pan Chan. "A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8734214.

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Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the mi

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44

Nie, Chun-Xiao. "Nonlinear Correlation Analysis of Time Series Based on Complex Network Similarity." International Journal of Bifurcation and Chaos 30, no. 15 (2020): 2050225. http://dx.doi.org/10.1142/s0218127420502259.

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Characterizing the relationship between time series is an important issue in many fields, in particular, in many cases there is a nonlinear correlation between series. This paper provides a new method to study the relationship between time series using the perspective of complex networks. This method converts a time series into a distance matrix and constructs a sequence of nearest neighbor networks, so that the nonlinear relationship between time series is expressed as similarity between networks. In addition, based on the surrogate series, we applied [Formula: see text]-score to characterize

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45

Fowler, Keirnan J. A., Suwash Chandra Acharya, Nans Addor, Chihchung Chou, and Murray C. Peel. "CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia." Earth System Science Data 13, no. 8 (2021): 3847–67. http://dx.doi.org/10.5194/essd-13-3847-2021.

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Abstract. This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) comprises data for 222 unregulated catchments, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large-scale changes, such as signif

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Weissteiner, Christof J., Raúl López-Lozano, Giacinto Manfron, et al. "A Crop Group-Specific Pure Pixel Time Series for Europe." Remote Sensing 11, no. 22 (2019): 2668. http://dx.doi.org/10.3390/rs11222668.

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Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop group

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Riyadi, Mohammad Alfan Alfian, Dian Sukma Pratiwi, Aldho Riski Irawan, and Kartika Fithriasari. "Clustering stationary and non-stationary time series based on autocorrelation distance of hierarchical and k-means algorithms." International Journal of Advances in Intelligent Informatics 3, no. 3 (2017): 154. http://dx.doi.org/10.26555/ijain.v3i3.98.

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Observing large dimension time series could be time-consuming. One identification and classification approach is a time series clustering. This study aimed to compare the accuracy of two algorithms, hierarchical cluster and K-Means cluster, using ACF’s distance for clustering stationary and non-stationary time series data. This research uses both simulation and real datasets. The simulation generates 7 stationary data models and another 7 of non-stationary data models. On the other hands, the real dataset is the daily temperature data in 34 cities in Indonesia. As a result, K-Means algorithm h

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Koochali, Alireza, Andreas Dengel, and Sheraz Ahmed. "If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN." Engineering Proceedings 5, no. 1 (2021): 40. http://dx.doi.org/10.3390/engproc2021005040.

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The contribution of this paper is two-fold. First, we present ProbCast—a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a framework that lets us transform a deterministic model into a probabilistic one with improved performance. The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN’s component carefully and efficiently. We conduc

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Arputhamary, B., and L. Arockiam. "A Pragmatic Study on Time Series Models for Big Data." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 67. http://dx.doi.org/10.23956/ijermt.v6i8.120.

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Recent years have witnessed the growth of Big Data, particularly Time Series data which initiates major research interest in Time Series analysis and forecasting future values. It finds interest in many applications such as business, stock market and exchange, weather forecasting, electricity demand, cost and usage of products and in any kind of place that has specific seasonal or trendy changes over time. The forecasting of Time Series data provides the organization with useful information that is necessary for making important decisions. In this paper, a detailed study is performed to find t

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50

Haimberger, Leopold. "Homogenization of Radiosonde Temperature Time Series Using Innovation Statistics." Journal of Climate 20, no. 7 (2007): 1377–403. http://dx.doi.org/10.1175/jcli4050.1.

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Abstract Radiosonde temperature records contain valuable information for climate change research from the 1940s onward. Since they are affected by numerous artificial shifts, time series homogenization efforts are required. This paper introduces a new technique that uses time series of temperature differences between the original radiosonde observations (obs) and background forecasts (bg) of an atmospheric climate data assimilation system for homogenization. These obs − bg differences, the “innovations,” are a by-product of the data assimilation process. They have been saved during the 40-yr E

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