Thematische Bibliographien / Serial autocorrelation

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Auswahl der wissenschaftlichen Literatur zum Thema „Serial autocorrelation“

Autor: Grafiati

Veröffentlicht am 4. Juni 2025

Zuletzt aktualisiert am 1. August 2025

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Zeitschriftenartikel zum Thema "Serial autocorrelation"

Hernández, Daniel †., and Julieta Rodríguez. "Bayesian surplus production model with serial autocorrelation." Marine and Fishery Sciences 32, no. 1 (2019): 31–41. https://doi.org/10.5281/zenodo.3925850.

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Presentation is made of a simple surplus production model called Surplus Production Model with Serial Autocorrelation (MPECAS in Spanish) since it considers as a unique assumption that the surplus production shows a serial correlation and has no explicit functional relation with biomass. Its application requires only an abundance index proportional to a given power of the actual mean abundance of the resource and the corresponding annual catches series. The estimate of the model parameters is presented within a Bayesian context using the SIR (Sampling Importance Resampling) algorithm. Simple r

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Lacal, Virginia, and Dag TjØstheim. "Local Gaussian Autocorrelation and Tests for Serial Independence." Journal of Time Series Analysis 38, no. 1 (2016): 51–71. http://dx.doi.org/10.1111/jtsa.12195.

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Ottenbacher, Kenneth J. "An Analysis of Serial Dependency in Occupational Therapy Research." Occupational Therapy Journal of Research 6, no. 4 (1986): 211–26. http://dx.doi.org/10.1177/153944928600600403.

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Single-subject and time-series designs have recently been advocated as a preferred method of examining clinical change in individual patients. Data from single-subject designs are frequently analyzed by means of graphic presentation and visual inspection. The presence of serial dependency or autocorrelation in data collected from a single individual can reduce the reliability and accuracy of visual inferences. Fifty-four data paths from single-subject research published in the occupational therapy literature were reviewed to determine the degree of serial dependency present in each data set. T

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Sugano, Osamu. "ON A SERIAL RANK TEST FOR RANDOMNESS AGAINST AUTOCORRELATION." Journal of the Japanese Society of Computational Statistics 4, no. 1 (1991): 25–34. http://dx.doi.org/10.5183/jjscs1988.4.25.

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Fang, Fei. "Stock Return Autocorrelation and Individual Equity Option Prices." Journal of Business Theory and Practice 9, no. 1 (2021): p51. http://dx.doi.org/10.22158/jbtp.v9n1p51.

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This study demonstrates empirically the impact of stock return autocorrelation on the prices of individual equity option. The option prices are characterized by the level and slope of implied volatility curves, and the stock return autocorrelation is measured by variance ratio and first-order serial return autocorrelation. Using a large sample of U.S. stocks, we show that there is a clear link between stock return autocorrelation and individual equity option prices: a higher stock return autocorrelation leads to a lower level of implied volatility (compared to realized volatility) and a steepe

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Wang, Mingliang, Jagtar Bhatti, Yonghe Wang, and Thierry Varem-Sanders. "Examining the Gain in Model Prediction Accuracy Using Serial Autocorrelation for Dominant Height Prediction." Forest Science 57, no. 3 (2011): 241–51. http://dx.doi.org/10.1093/forestscience/57.3.241.

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Abstract Within-subject serial correlation (autocorrelation) has long been a concern in forest growth and yield modeling but has been ignored for predictive purposes in most studies. In this study, we used linear prediction theory combined with linearized (with respect to random effects) nonlinear mixed models to investigate the improvement in model prediction achieved with autocorrelation. In this setting, predictions rely on estimates of common parameters obtained from a set of previous growth series and prior observations of new growth series, allowing the response variable for the new seri

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Wursten, Jesse. "Testing for Serial Correlation in Fixed-effects Panel Models." Stata Journal: Promoting communications on statistics and Stata 18, no. 1 (2018): 76–100. http://dx.doi.org/10.1177/1536867x1801800106.

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Current serial correlation tests for panel models are cumbersome to use, not suited for fixed-effects models, or limited to first-order autocorrelation. To fill this gap, I implement three recently developed tests.

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Lund, Robert, Xiaolan L. Wang, Qi Qi Lu, Jaxk Reeves, Colin Gallagher, and Yang Feng. "Changepoint Detection in Periodic and Autocorrelated Time Series." Journal of Climate 20, no. 20 (2007): 5178–90. http://dx.doi.org/10.1175/jcli4291.1.

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Abstract Undocumented changepoints (inhomogeneities) are ubiquitous features of climatic time series. Level shifts in time series caused by changepoints confound many inference problems and are very important data features. Tests for undocumented changepoints from models that have independent and identically distributed errors are by now well understood. However, most climate series exhibit serial autocorrelation. Monthly, daily, or hourly series may also have periodic mean structures. This article develops a test for undocumented changepoints for periodic and autocorrelated time series. Class

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Chan, Kwun Chuen Gary, Jinhui Han, Adrian Patrick Kennedy, and Sheung Chi Phillip Yam. "Testing network autocorrelation without replicates." PLOS ONE 17, no. 11 (2022): e0275532. http://dx.doi.org/10.1371/journal.pone.0275532.

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In this paper, we propose a portmanteau test for whether a graph-structured network dataset without replicates exhibits autocorrelation across units connected by edges. Specifically, the well known Ljung-Box test for serial autocorrelation of time series data is generalized to the network setting using a specially derived central limit theorem for a weakly stationary random field. The asymptotic distribution of the test statistic under the null hypothesis of no autocorrelation is shown to be chi-squared, yielding a simple and easy-to-implement procedure for testing graph-structured autocorrela

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Dirksen, Asger, Niels-Henrik Holstein-Rathlou, Flemming Madsen, et al. "Long-range correlations of serial FEV1 measurements in emphysematous patients and normal subjects." Journal of Applied Physiology 85, no. 1 (1998): 259–65. http://dx.doi.org/10.1152/jappl.1998.85.1.259.

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In obstructive lung disease the annual change in lung function is usually estimated from serial measurements of forced expiratory volume in 1 s (FEV1). Frequent measurements in each patient may not improve this estimate because data are not statistically independent; i.e., the measurements are autocorrelated. The purpose of this study was to describe the correlation structure in time series of FEV1 measurements. Nineteen patients with severe α1-antitrypsin deficiency (phenotype PiZ) and moderate to severe emphysema and two subjects with normal lungs were followed for several years with daily s

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Dissertationen zum Thema "Serial autocorrelation"

Rubilar, Torrealba Rolando Luis. "Portfolio performance : the case of serial autocorrelation." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/147682.

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TESIS PARA OPTAR AL GRADO DE MAGISTER EN FINANZAS The use of the Sharpe ratio for the measurement of the performance of the financial assets is widely generalized, although there is empirical evidence of serious problems with the assumptions behind the distribution functions. This paper explores the conditions under which the Sharpe ratio is efficient to analyze the performance of financial asset portfolios, a situation that is not true in the presence of strong autocorrelation. We demonstrate the effect that autocorrelation has in determining the best means of performance measurement,

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Zhao, Tao. "A new method for detection and classification of out-of-control signals in autocorrelated multivariate processes." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5615.

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Thesis (M.S.)--West Virginia University, 2008. Title from document title page. Document formatted into pages; contains x, 111 p. : ill. Includes abstract. Includes bibliographical references (p. 102-106).

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Liu, Gang. "A New Approach to ANOVA Methods for Autocorrelated Data." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1461226897.

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Paynter, Shayne. "Statistical changes in lakes in urbanizing watersheds and lake return frequencies adjusted for trend and initial stage utilizing generalized extreme value theory." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0002807.

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Malki, Rajaa. "Autocorrélations partielles empiriques d'une série vectorielle et application à la séparation de sources." Université Joseph Fourier (Grenoble), 1997. http://www.theses.fr/1997GRE10212.

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Cette these est consacree a l'etude des differentes formes d'autocorrelations partielles empiriques d'une serie chronologique vectorielle et a leur application a la separation aveugle d'un melange instantane de sources colorees. Dans la premiere partie, nous etudions les effets de la normalisation sur la definition des matrices d'autocorrelations partielles et sur leur estimation empirique dans le cas bivarie. Nous decrivons les principaux choix de racine carree des matrices de covariance et leurs consequences sur les differentes formes d'autocorrelation partielle theoriques. Nous analysons pa

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Assefa, Yared. "Time series and spatial analysis of crop yield." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15142.

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Master of Science Department of Statistics Juan Du Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. T

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Jaskowak, Daniel Joseph. "Detecting Transient Changes in Gait Using Fractal Scaling of Gait Variability in Conjunction with Gaussian Continuous Wavelet Transform." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87393.

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Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were

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Lobban, Stacey, and Hana Klimsova. "Demand Forecasting : A study at Alfa Laval in Lund." Thesis, Växjö University, School of Management and Economics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-2127.

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Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs.The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other m

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Strohe, Hans Gerhard. "Time series analysis : textbook for students of economics and business administration ; [part 2]." Universität Potsdam, 2004. http://stat.wiso.uni-potsdam.de/documents/zeitr/Time_Series_Analysis_Script2.pdf.

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Guarnieri, Jean Paulo. "EFICIÊNCIA DOS GRÁFICOS DE CONTROLE NA DETECÇÃO DE OUTLIERS EM PROCESSOS AUTORREGRESSIVOS E DE MÉDIAS MÓVEIS." Universidade Federal de Santa Maria, 2010. http://repositorio.ufsm.br/handle/1/8171.

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This research approaches the prediction models application along with the usage of residual control charts to evaluate productive processes with characteristics of autocorrelation in its samples. The overall objective was to determine the Individual Measurement Control Charts (IMCC) and the Exponentially Weighted Moving Average (EWMA) efficiency when applied to residuals of ARIMA class, to the outliers detection in autocorrelated processes, as well as identifying the autocorrelation influence and the amplitude of the outlier concerning the charts detection capacity. To each AR(1) and MA(1), 6

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Bücher zum Thema "Serial autocorrelation"

Robinson, P. M. Autocorrelation-robust inference. Suntory & Toyota International Centres for Economics & Related Disciplines, 1996.

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Hella, Heikki. On robust ESACF indentification [sic] of mixed ARIMA models. Bank of Finland, 2003.

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Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson, and Viktor Polyak. Econometric modeling in the GRETL package. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1732940.

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The tutorial describes the capabilities of the GRETL statistical package for computer data analysis and econometric modeling based on spatial data and time series. Using concrete economic examples, GRETL considers classical and generalized models of linear and nonlinear regression, methods for detecting and eliminating multicollinearity, models with variable structure, autoregressive processes, methods for testing and eliminating autocorrelation, as well as discrete choice models and systems of simultaneous equations. For the convenience of users, the tutorial contains all the task data

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Broersen, Petrus M. T. Automatic Autocorrelation and Spectral Analysis. Springer London, Limited, 2010.

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Broersen, Petrus M. T. Automatic Autocorrelation and Spectral Analysis. Springer, 2006.

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Trend analysis techniques. NASA Systems Assessment and Trend Analysis Division, Office of the Associate Administrator for Safety and Mission Quality, 1990.

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial a

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Beck, Nathaniel. Time‐Series Cross‐Section Methods. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0020.

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This article outlines the literature on time-series cross-sectional (TSCS) methods. First, it addresses time-series properties including issues of nonstationarity. It moves to cross-sectional issues including heteroskedasticity and spatial autocorrelation. The ways that TSCS methods deal with heterogeneous units through fixed effects and random coefficient models are shown. In addition, a discussion of binary variables and their relationship to event history models is provided. The best way to think about modeling single time series is to think about modeling the time-series component of TSCS

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Intervention Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0005.

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The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the interventio

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Mas, André, and Besnik Pumo. Linear Processes for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.3.

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This article provides an overview of the basic theory and applications of linear processes for functional data, with particular emphasis on results published from 2000 to 2008. It first considers centered processes with values in a Hilbert space of functions before proposing some statistical models that mimic or adapt the scalar or finite-dimensional approaches for time series. It then discusses general linear processes, focusing on the invertibility and convergence of the estimated moments and a general method for proving asymptotic results for linear processes. It also describes autoregressi

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Buchteile zum Thema "Serial autocorrelation"

Harrisson, Simon. "Autocorrelation in Multiblock Copolymers." In ACS Symposium Series. American Chemical Society, 2018. http://dx.doi.org/10.1021/bk-2018-1285.ch002.

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West, Kenneth D. "Heteroskedasticity and autocorrelation corrections." In Macroeconometrics and Time Series Analysis. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_15.

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Cipra, Tomas. "Autocorrelation Methods in Regression Models." In Time Series in Economics and Finance. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46347-2_7.

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Jensen, Willis A., and Jeffrey B. Birch. "Correlation and Autocorrelation in Profiles." In Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118071984.ch9.

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Knottnerus, Paul. "Estimation of the Sampling Autocorrelation ρz." In Springer Series in Statistics. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21764-2_8.

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Mitchell, Jennifer L., and Nancy L. Thompson. "High Order Autocorrelation in Fluorescence Correlation Spectroscopy." In Springer Series in Chemical Physics. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59542-4_21.

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Nicolau, Andréa Puzzi, Karen Dyson, David Saah, and Nicholas Clinton. "Exploring Lagged Effects in Time Series." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_21.

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AbstractIn this chapter, we will introduce lagged effects to build on the previous work in modeling time series data. Time-lagged effects occur when an event at one point in time impacts dependent variables at a later point in time. You will be introduced to concepts of autocovariance and autocorrelation, cross-covariance and cross-correlation, and auto-regressive models. At the end of this chapter, you will be able to examine how variables relate to one another across time and to fit time series models that take into account lagged events.

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Mitzner, R., W. Eberhardt, M. Neeb, et al. "Autocorrelation Experiments with Soft X-ray FEL Pulses." In Springer Series in Chemical Physics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-95946-5_46.

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Mikosch, Thomas, and Olivier Wintenberger. "Self-Normalization, Sample Autocorrelations and the Extremogram." In Extreme Value Theory for Time Series. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59156-3_10.

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Atienza, O. O., and L. C. Tang. "Simultaneous Monitoring of the Mean, Variance and Autocorrelation Structure of Serially Correlated Processes." In Six Sigma. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470062002.ch22.

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Konferenzberichte zum Thema "Serial autocorrelation"

Yang, Zhi, Likun Hou, and Xing Zhao. "Robust Autocorrelation for Period Detection in Time Series." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013092500003890.

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Wu, Guoyao, Zhiqiang Lan, Kun Zhou, et al. "Time-Series Adjusted FasterPAM Clustering of Enterprise Electricity Consumption Based on Autocorrelation Function." In 2024 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA). IEEE, 2024. http://dx.doi.org/10.1109/ispce-asia64773.2024.10756251.

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Chu, Jiaqi, Chengbao Liu, Jingwei Li, Yuan Li, and Jie Tan. "CorrDCN: Decomposed Convolutional Network with Seasonal Autocorrelation 2D-Variation Modeling for Time Series Forecasting." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650711.

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Hu, Yujie, Lingyu Zhu, Han Gong, and Xi Chen. "Data-Driven Dynamic Process Modeling Using Temporal RNN Incorporating Output Variable Autocorrelation and Stacked Autoencoder." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.150053.

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Dynamic process modeling in process industries has been extensively studied, especially with the development of deep learning techniques. Recurrent neural networks (RNN) and stacked autoencoders (SAE) are two powerful tools for dynamic modeling and data processing. However, most existing research primarily focuses on extracting features from process input data, often neglecting the temporal autocorrelation of output variables. In this work, a hierarchical model based on time-series RNN structure is proposed. The upper layer employs a long short-term memory (LSTM) network to extract temporal fe

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Schau, Kyle, Susan Polsky, and Gopal Gaonkar. "Helicopter Downwash Effects on Ship Airwake: Predictions, Modeling from a Database, and Simulation." In Vertical Flight Society 70th Annual Forum & Technology Display. The Vertical Flight Society, 2014. http://dx.doi.org/10.4050/f-0070-2014-9582.

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Helicopter downwash impact on ship airwake is addressed from a three-pronged approach: 1.) Predictions of one-point statistics of autospectrum and two-point statistics of cross-spectrum and coherence from a Computational Fluid Dynamics database of flow velocity predictions; 2.) Development of a mathematical framework for extracting interpretive autospectral models in closed form from a database; 3.) Simulation through white-noise-driven filters for the extracted models. The framework begins with an earlier-exercised perturbation series expansion of the autocorrelation for all three velocity co

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Schau, Kyle, and Gopal Gaonkar. "A Framework for Modeling Wind-Farm Wake Turbulence from a Database for Simulation and Analysis." In Vertical Flight Society 71st Annual Forum & Technology Display. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10302.

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This paper presents a mathematical framework for constructing interpretive autospectral models of wind-farm wake turbulence from a database; these models are in closed form, and the database refers to flow velocity points from experimental - wind-tunnel and full-scale, and computational fluid dynamics, investigations. The framework begins with a perturbation series expansion of the autocorrelations for all three velocity components; therein, the first term has a form of the von Karman longitudinal or lateral correlation function. These series are then transformed into equivalent series of auto

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Cusumano, J. P., D. Chelidze, and A. Chatterjee. "Experimental Application of a Method for Hidden Parameter Tracking in a Slowly Changing, Chaotic System." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1270.

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Abstract Results are presented of an experimental application of a method for tracking hidden parameters in slowly changing chaotic systems. The method exploits the time scale separation between fast dynamic variables and a slow drifting parameter. Locally linear tracking models are constructed using data from the reference system sampled on a fast time scale, employing delay coordinate embedding. These reference models are used to track parameter drift. The method is successfully applied to a forced oscillator with a two-well potential. The effect of the choice of prediction time interval is

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Vanem, Erik. "Analyzing Extreme Sea State Conditions by Time-Series Simulation." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-78795.

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Abstract This paper presents an extreme value analysis on data of significant wave height based on time-series simulation. A method to simulate time series with given marginal distribution and preserving the autocorrelation structure in the data is applied to significant wave height data. Then, extreme value analysis is performed by simulating from the fitted time-series model that preserves both the marginal probability distribution and the autocorrelation. In this way, the effect of serial correlation on the extreme values can be taken into account, without subsampling and de-clustering of t

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Morgan, Eugene. "Accounting for Serial Autocorrelation in Decline Curve Analysis of Marcellus Shale Gas Wells." In SPE/AAPG Eastern Regional Meeting. Society of Petroleum Engineers, 2018. http://dx.doi.org/10.2118/191788-18erm-ms.

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Garvin, C., and K. WAGNER. "Single pulse return radar signal identification with a multi-layer adaptive optical classifier." In Optics in Computing. Optica Publishing Group, 1997. http://dx.doi.org/10.1364/oc.1997.pd.4.

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We demonstrate an adaptive multi-layer optical classifier performing single pulse radar target recognition to identify isolated aircraft targets with varying orientation and/or range from the radar. The system uses optically-calculated time-frequency representations as its internal representation, and in particular the triple autocorrelation [1] due to the natual range invariance of this feature. This approach increases the separability of the input data by nonlinearly mapping it into a higher dimensional feature space. Serial processing of the optically computed feature vector using CCD detec

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Berichte der Organisationen zum Thema "Serial autocorrelation"

Samsonov, G. A., and G. S. Osipov. Calculating the average mutual information and autocorrelation function for time series in Wolfram Mathematica. Ljournal, 2020. http://dx.doi.org/10.18411/postulat-2020-7-19.

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Seema, Seema, Andreas Theocharis, and Andreas Kassler. Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstad University, 2024. http://dx.doi.org/10.59217/yjll7238.

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This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can fac

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