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Journal articles on the topic 'Time series aggregation'

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Published: 1 June 2024

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1

Rossana, Robert J., and John J. Seater. "Temporal Aggregation and Economic Time Series." Journal of Business & Economic Statistics 13, no. 4 (October 1995): 441. http://dx.doi.org/10.2307/1392389.

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2

Rossana, Robert J., and John J. Seater. "Temporal Aggregation and Economic Time Series." Journal of Business & Economic Statistics 13, no. 4 (October 1995): 441–51. http://dx.doi.org/10.1080/07350015.1995.10524618.

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3

Brännäs, Kurt, and Henry Ohlsson. "Asymmetric Time Series and Temporal Aggregation." Review of Economics and Statistics 81, no. 2 (May 1999): 341–44. http://dx.doi.org/10.1162/003465399558120.

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4

Yager, Ronald R. "Time Series Smoothing and OWA Aggregation." IEEE Transactions on Fuzzy Systems 16, no. 4 (August 2008): 994–1007. http://dx.doi.org/10.1109/tfuzz.2008.917299.

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5

de Jong, R., and S. de Bruin. "Time series of vegetation indices and the modifiable temporal unit problem." Biogeosciences Discussions 8, no. 4 (August 24, 2011): 8545–61. http://dx.doi.org/10.5194/bgd-8-8545-2011.

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Abstract. Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant systems. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1 % were found for annual periods, while deviations (with respect to seasonally adjusted data) increased up to 24 % for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results.
6

Celov, Dmitrij, and Remigijus Leipus. "Time series aggregation, disaggregation and long memory." Lietuvos matematikos rinkinys 46 (September 21, 2023): 255–62. http://dx.doi.org/10.15388/lmr.2006.30723.

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Large-scale aggregation and its inverse, disaggregation, problems are important in many fields of studies like macroeconomics, astronomy, hydrology and sociology. It was shown in Granger (1980) that a certain aggregation of random coefficient AR(1) models can lead to long memory output. Dacunha-Castelle and Oppenheim (2001) explored the topic further, answering when and if a predefined long memory process could be obtained as the result of aggregation of a specific class of individual processes. In this paper, the disaggregation scheme of Leipus et al. (2006) is briefly discussed. Then disaggregation into AR(1) is analyzed further, resulting in a theorem that helps, under corresponding assumptions, to construct a mixture density for a given aggregated by AR(1) scheme process. Finally the theorem is illustrated by FARUMA mixture densityÆs example.
7

Zhu, Ye, Yongjian Fu, and Huirong Fu. "Preserving Privacy in Time Series Data Mining." International Journal of Data Warehousing and Mining 7, no. 4 (October 2011): 64–85. http://dx.doi.org/10.4018/jdwm.2011100104.

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Time series data mining poses new challenges to privacy. Through extensive experiments, the authors find that existing privacy-preserving techniques such as aggregation and adding random noise are insufficient due to privacy attacks such as data flow separation attack. This paper also presents a general model for publishing and mining time series data and its privacy issues. Based on the model, a spectrum of privacy preserving methods is proposed. For each method, effects on classification accuracy, aggregation error, and privacy leak are studied. Experiments are conducted to evaluate the performance of the methods. The results show that the methods can effectively preserve privacy without losing much classification accuracy and within a specified limit of aggregation error.
8

Beran, Jan, Haiyan Liu, and Sucharita Ghosh. "On aggregation of strongly dependent time series." Scandinavian Journal of Statistics 47, no. 3 (December 13, 2019): 690–710. http://dx.doi.org/10.1111/sjos.12421.

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9

Kim, Hung Soo, Yong Nam Yoon, Gyu-Sei Yi, and Taegyun Kim. "Effect of aggregation on chaotic time series." KSCE Journal of Civil Engineering 4, no. 4 (December 2000): 219–26. http://dx.doi.org/10.1007/bf02823969.

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10

Chipman, J., and P. Winker. "Optimal aggregation of linear time series models." Computational Statistics & Data Analysis 49, no. 2 (April 2005): 311–31. http://dx.doi.org/10.1016/j.csda.2004.05.015.

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11

Celov, D., R. Leipus, and A. Philippe. "Time series aggregation, disaggregation, and long memory." Lithuanian Mathematical Journal 47, no. 4 (October 2007): 379–93. http://dx.doi.org/10.1007/s10986-007-0026-6.

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12

de Jong, R., and S. de Bruin. "Linear trends in seasonal vegetation time series and the modifiable temporal unit problem." Biogeosciences 9, no. 1 (January 5, 2012): 71–77. http://dx.doi.org/10.5194/bg-9-71-2012.

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Abstract. Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant productivity. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1% were found for annual periods (with respect to seasonally adjusted data), while deviations increased up to 24% for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results.
13

Stehle, Samuel. "Temporal aggregation bias and Gerrymandering urban time series." GeoInformatica 26, no. 1 (November 13, 2021): 233–52. http://dx.doi.org/10.1007/s10707-021-00452-z.

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14

Chambers, Marcus J. "Long Memory and Aggregation in Macroeconomic Time Series." International Economic Review 39, no. 4 (November 1998): 1053. http://dx.doi.org/10.2307/2527352.

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15

Maliar, Lilia, and Serguei Maliar. "Preference shocks from aggregation: time series data evidence." Canadian Journal of Economics/Revue Canadienne d`Economique 37, no. 3 (August 2004): 768–81. http://dx.doi.org/10.1111/j.0008-4085.2004.00247.x.

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16

Hotta, Luiz K., and Klaus L. Vasconcellos. "Aggregation and Disaggregation of Structural Time Series Models." Journal of Time Series Analysis 20, no. 2 (March 1999): 155–71. http://dx.doi.org/10.1111/1467-9892.00131.

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17

Chan, W. S., S. H. Cheung, L. X. Zhang, and K. H. Wu. "Temporal aggregation of equity return time-series models." Mathematics and Computers in Simulation 78, no. 2-3 (July 2008): 172–80. http://dx.doi.org/10.1016/j.matcom.2008.01.010.

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18

Alarcon Falconi, Tania M., Bertha Estrella, Fernando Sempértegui, and Elena N. Naumova. "Effects of Data Aggregation on Time Series Analysis of Seasonal Infections." International Journal of Environmental Research and Public Health 17, no. 16 (August 13, 2020): 5887. http://dx.doi.org/10.3390/ijerph17165887.

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Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.
19

Sbrana, Giacomo. "Structural time series models and aggregation: some analytical results." Journal of Time Series Analysis 32, no. 3 (November 15, 2010): 315–16. http://dx.doi.org/10.1111/j.1467-9892.2010.00701.x.

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20

Reddi, Sivaranjani. "Privacy Preserving Data Mining Using Time Series Data Aggregation." International Journal of Strategic Information Technology and Applications 8, no. 4 (October 2017): 1–15. http://dx.doi.org/10.4018/ijsita.2017100101.

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This article proposes a mechanism to provide privacy to mined results by assuming that the data is distributed across many nodes. The first objective includes mining the query results by the node in a cluster, communicating it to the cluster head, aggregating the data collected from all the cluster nodes and then communicating it to the group controller. The second objective is to incorporate privacy at each level of the clusters node: cluster head and the group controller level. The final objective is to provide a dynamic network feature, where the nodes can join or leave the distributed network without disturbing the network functionality. The proposed algorithm was implemented and validated in Java for its performance in terms of communication costs computational complexity.
21

Jie, Wang, and Lu Jingyi. "Multi-threads computation for aggregation of time-series data." International Journal of Wireless and Mobile Computing 16, no. 1 (2019): 14. http://dx.doi.org/10.1504/ijwmc.2019.097416.

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22

Jie, Wang, and Lu Jingyi. "Multi-threads computation for aggregation of time-series data." International Journal of Wireless and Mobile Computing 16, no. 1 (2019): 14. http://dx.doi.org/10.1504/ijwmc.2019.10018539.

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23

Lin, Gwo-Fong, and Fong-Chung Lee. "An aggregation-disaggregation approach for hydrologic time series modelling." Journal of Hydrology 138, no. 3-4 (October 1992): 543–57. http://dx.doi.org/10.1016/0022-1694(92)90136-j.

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24

Johannes, Mark R. S. "Prey Aggregation Is Correlated with Increased Predation Pressure in Lake Fish Communities." Canadian Journal of Fisheries and Aquatic Sciences 50, no. 1 (January 1, 1993): 66–73. http://dx.doi.org/10.1139/f93-008.

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Aggregations of prey fish, golden shiner (Notemigonus crysoleucas), were examined during 7 yr of predator manipulations in two lakes to determine whether they responded to changes in predation pressure and varied with time-of-day, age, and habitat. Regression analysis was used to examine aggregation in 12 replicate prey densities from two time periods, two ages, two habitats, three sample series, and seven predator densities. Aggregation was assessed as the variance of mean densities for each treatment combination. Multiple regression and ANCOVA analyses indicated that (1) golden shiner aggregated more during day than night, (2) their aggregation was positively related to predator density, (3) young shiner aggregated more than older ones at low predator densities, and (4) aggregation in older shiner was more responsive to increased predator densities than aggregation in younger shiner. These results provide empirical evidence that golden shiner aggregation patterns respond to predation pressure and the response varies with time and age. These results also suggest that variance in net catches can provide an index of fish aggregation and that aggregation observed at the population level is not solely dependent on species and density, but is a behavioural response mediated by several factors including predators.
25

BALLINI, R., and R. R. YAGER. "LINEAR DECAYING WEIGHTS FOR TIME SERIES SMOOTHING: AN ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 01 (February 2014): 23–40. http://dx.doi.org/10.1142/s0218488514500020.

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In this paper, we investigate the use of weighted averaging aggregation operators as techniques for time series smoothing. We analyze the moving average, exponential smoothing methods, and a new class of smoothing operators based on linearly decaying weights from the perspective of ordered weights averaging to estimate a constant model. We examine two important features associated with the smoothing processes: the average age of the data and the expected variance, both defined in terms of the associated weights. We show that there exists a fundamental conflict between keeping the variance small while using the freshest data. We illustrate the flexibility of the smoothing methods with real datasets; that is, we evaluate the aggregation operators with respect to their minimal attainable variance versus average age. We also examine the efficiency of the smoothed models in time series smoothing, considering real datasets. Good smoothing generally depends upon the underlying method's ability to select appropriate weights to satisfy the criteria of both small variance and recent data.
26

Araujo, Mayara Ribeiro de, David dos Santos Martins, Maurício José Fornazier, Keiko Uramoto, Paulo Sérgio Fiuza Ferreira, Roberto Antonio Zucchi, and Wesley Augusto Conde Godoy. "Aggregation and spatio-temporal dynamics of fruit flies (Diptera, Tephritidae) in papaya orchards associated with different area delimitations in Brazil." Acta Scientiarum. Agronomy 44 (December 21, 2021): e53466. http://dx.doi.org/10.4025/actasciagron.v44i1.53466.

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We investigated aggregation patterns in three fruit fly species economically important in Brazil, namely Ceratitis capitata, Anastrepha fraterculus, and A. obliqua. The study was carried out in a buffer zone and two neighbourhoods by comparing two-time series associated with the management strategy of fruit flies (systems approach). The abundance of these three species significantly decreased over the years with a negative binomial regression model describing the relationship between abundance and time in the entire area, buffer zone, and their neighbourhoods. In addition, the negative binomial model was also well fitted to the frequency distribution data of fruit flies in all analyzed scenarios. Anastrepha obliqua showed the highest aggregation degree, considering both the entire area and time series. A. fraterculus exhibited the lowest aggregation level, and C. capitata showed an intermediate degree. The buffer zone exhibited the highest aggregation degree for all species, and neighbourhood 2 exhibited the lowest aggregation degree. The aggregation degree was higher in the time series impacted by the systems approach than the series in the first years of its implementation.
27

Manasa, T. "Sum Aggregation of Time – Series Data with new Preserving System." International Journal of Computer Trends and Technology 27, no. 1 (September 25, 2015): 51–57. http://dx.doi.org/10.14445/22312803/ijctt-v27p109.

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28

Xu, Paiheng, Rong Zhang, and Yong Deng. "A novel weight determination method for time series data aggregation." Physica A: Statistical Mechanics and its Applications 482 (September 2017): 42–55. http://dx.doi.org/10.1016/j.physa.2017.04.028.

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29

Elsworth, Steven, and Stefan Güttel. "ABBA: adaptive Brownian bridge-based symbolic aggregation of time series." Data Mining and Knowledge Discovery 34, no. 4 (June 3, 2020): 1175–200. http://dx.doi.org/10.1007/s10618-020-00689-6.

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30

Kotzur, Leander, Peter Markewitz, Martin Robinius, and Detlef Stolten. "Time series aggregation for energy system design: Modeling seasonal storage." Applied Energy 213 (March 2018): 123–35. http://dx.doi.org/10.1016/j.apenergy.2018.01.023.

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31

Gonzalez, P. "Temporal aggregation and systematic sampling in structural time-series models." Long Range Planning 26, no. 1 (February 1993): 151. http://dx.doi.org/10.1016/0024-6301(93)90285-n.

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32

Kőrösi, Gábor, László Lovrics, and László Mátyás. "Aggregation and the long run properties of economic time series." Mathematics and Computers in Simulation 39, no. 3-4 (November 1995): 279–86. http://dx.doi.org/10.1016/0378-4754(94)00071-6.

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33

González, Pilar. "Temporal aggregation and systematic sampling in structural time-series models." Journal of Forecasting 11, no. 4 (June 1992): 271–81. http://dx.doi.org/10.1002/for.3980110403.

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34

Shellman, Stephen M. "Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis." Political Analysis 12, no. 1 (2004): 97–104. http://dx.doi.org/10.1093/pan/mpg017.

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While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, “Event Data Analysis and Threats from Temporal Aggregation”) political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.
35

Kaltsounis, Anastasios, Evangelos Spiliotis, and Vassilios Assimakopoulos. "Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning." Algorithms 16, no. 4 (April 12, 2023): 206. http://dx.doi.org/10.3390/a16040206.

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We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for producing forecasts or to derive weights to properly combine the forecasts generated at various levels. The classifier consists a meta-learner that correlates key time series features with forecasting accuracy, thus enabling a dynamic, data-driven selection or combination. Our experiments, conducted in two large data sets of slow- and fast-moving series, indicate that the proposed meta-learner can outperform standard forecasting approaches.
36

Ventero, Ana, Magdalena Iglesias, and Pilar Córdoba. "Krill spatial distribution in the Spanish Mediterranean Sea in summer time." Journal of Plankton Research 41, no. 4 (July 2019): 491–505. http://dx.doi.org/10.1093/plankt/fbz030.

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Abstract We documented krill distribution in the Spanish Mediterranean Sea for the first time using acoustic methods, highlighting the method’s suitability to study marine communities restricted to specific areas with patchy aggregation behavior. The 2009–2017 acoustic time series analysis revealed that krill distribution, mainly located on the continental shelf edge, was driven by the presence of fronts and submarine canyons. On the other hand, areas of persistent krill distribution included from Cape La Nao to the eastern part of Almeria Bay, although an interannual northwards increase of krill presence had been detected in 2015–2017 likely related to the position of the Balearic front. We provide information on the aggregation characteristics and biological parameters of three krill species, Nyctiphanes couchii, Nematoscelis megalops and Meganyctiphanes norvegica. N. couchii and N. megalops formed patchy pelagic aggregations in the neritic and oceanic zone, respectively, and they were the most common species in the net tows. By contrast, M. norvegica formed a large demersal aggregation on the continental shelf edge and was only found in 2017; nevertheless, its 861-kg catch represented a unique milestone in the Mediterranean. Finally, krill species shared distribution area with Maurolicus muelleri; thus, coexistence between them are also described.
37

Hoffmann, Maximilian, Leander Kotzur, Detlef Stolten, and Martin Robinius. "A Review on Time Series Aggregation Methods for Energy System Models." Energies 13, no. 3 (February 3, 2020): 641. http://dx.doi.org/10.3390/en13030641.

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Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.
38

عبد الغنی, سمر أحمد حلمی عبد الغنی. "Horizontal and vertical Aggregation Bias of time series with empirical study." المجلة العلمیة للدراسات والبحوث المالیة والتجاریة 3, no. 1 (January 1, 2022): 397–428. http://dx.doi.org/10.21608/cfdj.2021.207387.

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39

Silvestrini, Andrea, and David Veredas. "TEMPORAL AGGREGATION OF UNIVARIATE AND MULTIVARIATE TIME SERIES MODELS: A SURVEY." Journal of Economic Surveys 22, no. 3 (July 2008): 458–97. http://dx.doi.org/10.1111/j.1467-6419.2007.00538.x.

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40

BREITUNG, JORG, and NORMAN R. SWANSON. "Temporal aggregation and spurious instantaneous causality in multiple time series models." Journal of Time Series Analysis 23, no. 6 (November 2002): 651–65. http://dx.doi.org/10.1111/1467-9892.00284.

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41

Benhamouda, Fabrice, Marc Joye, and BenoîT Libert. "A New Framework for Privacy-Preserving Aggregation of Time-Series Data." ACM Transactions on Information and System Security 18, no. 3 (April 14, 2016): 1–21. http://dx.doi.org/10.1145/2873069.

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42

Kirchgässner, G., and J. Wolters. "Implications of temporal aggregation on the relation between two time series." Statistical Papers 33, no. 1 (December 1992): 1–19. http://dx.doi.org/10.1007/bf02925307.

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43

Alvisi, S., N. Ansaloni, and M. Franchini. "A Procedure for Spatial Aggregation of Synthetic Water Demand Time Series." Procedia Engineering 70 (2014): 51–60. http://dx.doi.org/10.1016/j.proeng.2014.02.007.

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44

Kacprzyk, J., A. Wilbik, and S. Zadrożny. "Linguistic summarization of time series using a fuzzy quantifier driven aggregation." Fuzzy Sets and Systems 159, no. 12 (June 2008): 1485–99. http://dx.doi.org/10.1016/j.fss.2008.01.025.

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45

Tsiporkova, E., and V. Boeva. "Fusing time series expression data through hybrid aggregation and hierarchical merge." Bioinformatics 24, no. 16 (August 9, 2008): i63—i69. http://dx.doi.org/10.1093/bioinformatics/btn264.

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46

Qiao, Jialin, Xiangdong Huang, Jianmin Wang, and Raymond K. Wong. "Dual-PISA: An index for aggregation operations on time series data." Information Systems 87 (January 2020): 101427. http://dx.doi.org/10.1016/j.is.2019.101427.

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47

Li, Yongkai, Shubo Liu, Jun Wang, and Mengjun Liu. "Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol." International Journal of Distributed Sensor Networks 12, no. 7 (July 2016): 1341606. http://dx.doi.org/10.1177/155014771341606.

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48

Garg, Bindu, and Rohit Garg. "Enhanced accuracy of fuzzy time series model using ordered weighted aggregation." Applied Soft Computing 48 (November 2016): 265–80. http://dx.doi.org/10.1016/j.asoc.2016.07.002.

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49

Miranda, Karen, and Victor Ramos. "Improving data aggregation in Wireless Sensor Networks with time series estimation." IEEE Latin America Transactions 14, no. 5 (May 2016): 2425–32. http://dx.doi.org/10.1109/tla.2016.7530441.

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50

Bahl, Björn, Julian Lützow, David Shu, Dinah Elena Hollermann, Matthias Lampe, Maike Hennen, and André Bardow. "Rigorous synthesis of energy systems by decomposition via time-series aggregation." Computers & Chemical Engineering 112 (April 2018): 70–81. http://dx.doi.org/10.1016/j.compchemeng.2018.01.023.

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