scholarly journals Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1832
Author(s):  
Mariano Méndez-Suárez
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Marketing Mix ◽  
Structural Equations ◽  
Superior Performance ◽  
Parameter Estimates ◽  
Significance Analysis ◽  
Autocorrelation Structure ◽  
Multichannel Retailing ◽  
Black Friday

Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing mix modeling, sampling bootstrapping shows inconsistencies that arise because the series has an autocorrelation structure and contains seasonal events, such as Christmas or Black Friday, especially in multichannel retailing, making the significance analysis of the PLS-SEM model unreliable. The alternative proposed in this research uses maximum entropy bootstrapping (meboot), a technique specifically designed for time series, which maintains the autocorrelation structure and preserves the occurrence over time of seasonal events or structural changes that occurred in the original series in the bootstrapped series. The results showed that meboot had superior performance than sampling bootstrapping in terms of the coherence of the bootstrapped data and the quality of the significance analysis.

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

scholarly journals Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sahamoddin Khailaie ◽  
Tanmay Mitra ◽  
Arnab Bandyopadhyay ◽  
Marta Schips ◽  
Pietro Mascheroni ◽  
...  
Keyword(s):  
Structural Changes ◽  
Reproduction Number ◽  
National Level ◽  
Transmission Dynamics ◽  
Federal State ◽  
Parameter Estimates ◽  
Time Varying ◽  
Short Term ◽  
Robert Koch Institute ◽  
Federal States

Abstract Background SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression. Methods We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute. Results The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2–3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes. Conclusions The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

A Standard Aggregative Model for the Jordanian Economy

1985 ◽  
Vol 17 (1) ◽  
pp. 67-88
Author(s):  
W. M. Mikhail
Keyword(s):  
Time Series ◽  
Simple Model ◽  
Structural Changes ◽  
Time Series Model ◽  
National Accounts ◽  
Macroeconomic Variables

The simple model presented in this paper is an econometric time-series model which was designed to use the available Jordanian national accounts statistics. It aims at explaining the structural changes in the Jordanian economy in the 1970s as well as projecting values of certain macroeconomic variables for the year 1985, that being the terminal year in the current 5-year plan.

scholarly journals COMPARATIVE ANALYSIS OF DIFFERENT CRITERIA OF SITE STRUCTURAL STABILITY IN THE TIME SERIES

2019 ◽  
pp. 119-128
Author(s):  
Liliya Andreevna Landman ◽  
Andrei Vladimirovich Faddeenkov
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Structural Changes ◽  
Least Squares Method ◽  
Complex Structure ◽  
Standard Normal Distribution ◽  
Regression Equations ◽  
Unknown Parameters ◽  
Chow Test ◽  
Wide Range

The concept of structure is used to describe a set of stable relations between the main parts of the object, which describe its integrity and identity, i.e, preserving the basic properties for a wide range of internal and external changes. This concept usually relates to the concepts of system and organization. The structure expresses a stable part of the system that is slightly changed during different reforms. Over the years structural changes take place because of active economic policy or as a result of spontaneous, uncontrollable processes. Therefore, it seems to be quite natural to find out whether there have been structural changes in the observation period, and to find them reflected in the specification of the model. The basic ideas of methods for determining structural changes in the time series dynamics have been considered, such as Chow test, Gujarati test and Poirier method. The power study was conducted for the three possible cases of change in time series trends. The random error was modeled according to the standard normal distribution. A linear multiple regression model with three independent variables was used as a time series model. Estimation of the vector of unknown parameters of the model was conducted using least squares method. For each of the three criteria the of test the null hypothesis about time series instability was carried out using the F -criterion, which involves finding the residual sum of squares of a regression model and analysis of correlation between its decline and the loss of degrees of freedom. It can be noted that Gujarati and Poirier equations have a more complex structure than equation of Chow test; however, using Chow test assumes estimation of the parameters of the three regression equations.

scholarly journals Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

2017 ◽  
Vol 33 (1) ◽  
pp. 155-186
Author(s):  
Marcela Cohen Martelotte ◽  
Reinaldo Castro Souza ◽  
Eduardo Antônio Barros da Silva
Keyword(s):  
Time Series ◽  
Good Alternative ◽  
Superior Performance ◽  
Seasonal Adjustment ◽  
Artificial Data ◽  
Seasonal Fluctuations ◽  
Seasonal Behaviour ◽  
Macroeconomic Time Series ◽  
The One ◽  
Made In

Abstract Considering that many macroeconomic time series present changing seasonal behaviour, there is a need for filters that are robust to such changes. This article proposes a method to design seasonal filters that address this problem. The design was made in the frequency domain to estimate seasonal fluctuations that are spread around specific bands of frequencies. We assessed the generated filters by applying them to artificial data with known seasonal behaviour based on the ones of the real macroeconomic series, and we compared their performance with the one of X-13A-S. The results have shown that the designed filters have superior performance for series with pronounced moving seasonality, being a good alternative in these cases.

scholarly journals Ordinary kriging as a tool to estimate historical daily streamflow records

2016 ◽  
Vol 20 (7) ◽  
pp. 2721-2735 ◽  
Author(s):  
William H. Farmer
Keyword(s):  
Time Series ◽  
Ordinary Kriging ◽  
Order Approximation ◽  
Superior Performance ◽  
Daily Streamflow ◽  
History Of ◽  
First Order Approximation ◽  
And Behavior ◽  
Kriging Approach ◽  
Daily Streamflow Time Series

Abstract. Efficient and responsible management of water resources relies on accurate streamflow records. However, many watersheds are ungaged, limiting the ability to assess and understand local hydrology. Several tools have been developed to alleviate this data scarcity, but few provide continuous daily streamflow records at individual streamgages within an entire region. Building on the history of hydrologic mapping, ordinary kriging was extended to predict daily streamflow time series on a regional basis. Pooling parameters to estimate a single, time-invariant characterization of spatial semivariance structure is shown to produce accurate reproduction of streamflow. This approach is contrasted with a time-varying series of variograms, representing the temporal evolution and behavior of the spatial semivariance structure. Furthermore, the ordinary kriging approach is shown to produce more accurate time series than more common, single-index hydrologic transfers. A comparison between topological kriging and ordinary kriging is less definitive, showing the ordinary kriging approach to be significantly inferior in terms of Nash–Sutcliffe model efficiencies while maintaining significantly superior performance measured by root mean squared errors. Given the similarity of performance and the computational efficiency of ordinary kriging, it is concluded that ordinary kriging is useful for first-order approximation of daily streamflow time series in ungaged watersheds.

Long-term aging characteristics of co-filled nano-silica and micro-ATH in HTV silicone rubber composite insulators

2020 ◽  
Vol 29 (1) ◽  
pp. 40-56 ◽  
Author(s):  
Arooj Rashid ◽  
Jawad Saleem ◽  
Muhammad Amin ◽  
Sahibzada Muhammad Ali
Keyword(s):  
Mechanical Properties ◽  
Aging Time ◽  
Structural Changes ◽  
Erosion Resistance ◽  
Breakdown Strength ◽  
Accelerated Aging ◽  
Dielectric Strength ◽  
Superior Performance ◽  
Wave Number

Multiple environmental stresses produce complex phenomena of aging in polymeric insulators. The main aim of this research is to investigate the improved aging characteristics of silica (SiO2)/alumina trihydrate (ATH) hybrid samples (HSs) in high-temperature vulcanized rubber. For this purpose, three HSs comprising 20% micro-ATH with 2% nano-SiO2 (S2), 4% nano-SiO2 (S4), 6% nano-SiO2 (S6) along with sample-virgin (SV) are subjected to long-term accelerated aging of 9000 h. A special aging chamber is fabricated for the aging process of samples. The aging characteristics of these samples are investigated by measuring leakage current (LC) and hydrophobicity classification (HC) after every weathering cycle. Similarly, Fourier transform infrared (FTIR) spectroscopy is performed to observe the important structural changes over the entire aging time. The dielectric strength of AC is also performed after every 1000 h of aging. Tracking and erosion resistance and mechanical properties are also investigated before and after aging. From the critical investigation, it is observed that HSs possess improved results in all the conducted tests. S2 has the lowest LC and HC values throughout the aging time. Similarly, S6 described the highest breakdown strength at the end of the accelerated aging. In the case of FTIR, it is analyzed that the important wave numbers remain intact for all the HSs in the accelerated aging environment. The loss percentage in the wave number for SV is higher, compared to the HSs. After performing the tracking and erosion resistance test, HSs have superior performance. For some of the mechanical properties, HSs showed improved values. Thus, from the experimental analysis, it is deducted that the sample S2 offers the highest resistance to the aging conditions, compared to the SV and other HSs.

Challenges in Nonlinear Structural Equation Modeling

Methodology ◽  
2007 ◽  
Vol 3 (3) ◽  
pp. 100-114 ◽  
Author(s):  
Polina Dimitruk ◽  
Karin Schermelleh-Engel ◽  
Augustin Kelava ◽  
Helfried Moosbrugger
Keyword(s):  
Structural Equation Modeling ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Nonlinear Effects ◽  
Structural Equations ◽  
Estimation Methods ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
Nonlinear Structural ◽  
Nonlinear Structural Equation

Abstract. Challenges in evaluating nonlinear effects in multiple regression analyses include reliability, validity, multicollinearity, and dichotomization of continuous variables. While reliability and validity issues are solved by employing nonlinear structural equation modeling, multicollinearity remains a problem which may even be aggravated when using latent variable approaches. Further challenges of nonlinear latent analyses comprise the distribution of latent product terms, a problem especially relevant for approaches using maximum likelihood estimation methods based on multivariate normally distributed variables, and unbiased estimates of nonlinear effects under multicollinearity. The only methods that explicitly take the nonnormality of nonlinear latent models into account are latent moderated structural equations (LMS) and quasi-maximum likelihood (QML). In a small simulation study both methods yielded unbiased parameter estimates and correct estimates of standard errors for inferential statistics. The advantages and limitations of nonlinear structural equation modeling are discussed.

scholarly journals Bayesian Sigmoid-Type Time Series Forecasting with Missing Data for Greenhouse Crops

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3246 ◽  
Author(s):  
Alexander Kocian ◽  
Giulia Carmassi ◽  
Fatjon Cela ◽  
Luca Incrocci ◽  
Paolo Milazzo ◽  
...  
Keyword(s):  
Time Series ◽  
Environmental Control ◽  
Measurement Data ◽  
Environmental Parameters ◽  
Integrated Approach ◽  
Crop Growth ◽  
Parameter Estimates ◽  
Sigmoid Function ◽  
Area Index ◽  
Hidden States

This paper follows an integrated approach of Internet of Things based sensing and machine learning for crop growth prediction in agriculture. A Dynamic Bayesian Network (DBN) relates crop growth associated measurement data to environmental control data via hidden states. The measurement data, having (non-linear) sigmoid-type dynamics, are instances of the two classes observed and missing, respectively. Considering that the time series of the logistic sigmoid function is the solution to a reciprocal linear dynamic model, the exact expectation-maximization algorithm can be applied to infer the hidden states and to learn the parameters of the model. At iterative convergence, the parameter estimates are then used to derive a predictor of the measurement data several days ahead. To evaluate the performance of the proposed DBN, we followed three cultivation cycles of micro-tomatoes (MicroTom) in a mini-greenhouse. The environmental parameters were temperature, converted into Growing Degree Days (GDD), and the solar irradiance, both at a daily granularity. The measurement data were Leaf Area Index (LAI) and Evapotranspiration (ET). Although measurement data were only available scarcely, it turned out that high quality measurement data predictions were possible up to three weeks ahead.

Sign in / Sign up

Export Citation Format

Share Document