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.

Implement multi-step-ahead forecasting with multi-point association fuzzy logical relationship for time series

2021 ◽  
pp. 1-17
Author(s):  
Fang Li ◽  
Lihua Zhang ◽  
Xiao Wang ◽  
Shihu Liu
Keyword(s):  
Time Series ◽  
High Order ◽  
Fuzzy Time Series ◽  
Superior Performance ◽  
Forecasting Model ◽  
Logical Relationship ◽  
Multiple Observations ◽  
One Step ◽  
The One ◽  
Fuzzy Logical

In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis.

Simulation of the internal temperature in the Passol Laboratory, University of Debrecen

2012 ◽  
Vol 3 (1) ◽  
pp. 63-73 ◽  
Author(s):  
I. Csáky ◽  
F. Kalmár
Keyword(s):  
Temperature Variation ◽  
Air Temperature ◽  
Indoor Air ◽  
Storage Capacity ◽  
Building Structures ◽  
Internal Temperature ◽  
The One ◽  
Heavy Structure ◽  
East West ◽  
Made In

Abstract Nowadays the facades of newly built buildings have significant glazed surfaces. The solar gains in these buildings can produce discomfort caused by direct solar radiation on the one hand and by the higher indoor air temperature on the other hand. The amplitude of the indoor air temperature variation depends on the glazed area, orientation of the facade and heat storage capacity of the building. This paper presents the results of a simulation, which were made in the Passol Laboratory of University of Debrecen in order to define the internal temperature variation. The simulation proved that the highest amplitudes of the internal temperature are obtained for East orientation of the facade. The upper acceptable limit of the internal air temperature is exceeded for each analyzed orientation: North, South, East, West. Comparing different building structures, according to the obtained results, in case of the heavy structure more cooling hours are obtained, but the energy consumption for cooling is lower.

scholarly journals Theoretical Modelling of Ion Exchange Processes in Glass: Advances and Challenges

2021 ◽  
Vol 11 (11) ◽  
pp. 5070
Author(s):  
Xesús Prieto-Blanco ◽  
Carlos Montero-Orille
Keyword(s):  
Ion Exchange ◽  
Diffusion Coefficients ◽  
Molten Salts ◽  
Theoretical Modelling ◽  
Copper Films ◽  
Theoretical Frameworks ◽  
Self Diffusion ◽  
Exchange Processes ◽  
The One ◽  
Made In

In the last few years, some advances have been made in the theoretical modelling of ion exchange processes in glass. On the one hand, the equations that describe the evolution of the cation concentration were rewritten in a more rigorous manner. This was made into two theoretical frameworks. In the first one, the self-diffusion coefficients were assumed to be constant, whereas, in the second one, a more realistic cation behaviour was considered by taking into account the so-called mixed ion effect. Along with these equations, the boundary conditions for the usual ion exchange processes from molten salts, silver and copper films and metallic cathodes were accordingly established. On the other hand, the modelling of some ion exchange processes that have attracted a great deal of attention in recent years, including glass poling, electro-diffusion of multivalent metals and the formation/dissolution of silver nanoparticles, has been addressed. In such processes, the usual approximations that are made in ion exchange modelling are not always valid. An overview of the progress made and the remaining challenges in the modelling of these unique processes is provided at the end of this review.

scholarly journals Modeling and evaluating beat gestures for social robots

2021 ◽  
Author(s):  
Unai Zabala ◽  
Igor Rodriguez ◽  
José María Martínez-Otzeta ◽  
Elena Lazkano
Keyword(s):  
Motion Capture ◽  
Motion Capture System ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Desirable Feature ◽  
Quantitative Results ◽  
Path Lengths ◽  
End Effectors ◽  
The One ◽  
Made In

AbstractNatural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation.

scholarly journals When to Substitute a Missing Upper Lateral With a Canine: A Simplified Approach

2021 ◽  
pp. 030157422098054
Author(s):  
Renu Datta
Keyword(s):  
Anterior Segment ◽  
Specific Treatment ◽  
Simplified Approach ◽  
Special Cases ◽  
Randomized Control ◽  
The One ◽  
Individual Presentation ◽  
Available Information ◽  
Missing Tooth ◽  
Made In

Introduction: The upper lateral incisor is the most commonly missing tooth in the anterior segment. It leads to esthetic and functional imbalance for the patients. The ideal solution is the one that is most conservative and which fulfills the functional and esthetic needs of the concerned individual. Canine substitution is evolving to be the treatment of choice in most of the cases, because of its various advantages. These are special cases that need more time and effort from the clinicians due to space discrepancy in the upper and lower arches, along with the presentation of individual malocclusion. Aims and Objectives: Malocclusion occurring due to missing laterals is more complex, needing more time and effort from the clinicians because of space discrepancy, esthetic compromise, and individual presentation of the malocclusion. An attempt has been made in this article to review, evaluate, and tabulate the important factors for the convenience of clinicians. Method: All articles related to canine substitution were searched in the electronic database PubMed, and the important factors influencing the decision were reviewed. After careful evaluation, the checklist was evolved. Result: The malocclusions in which canine substitution is the treatment of choice are indicated in the tabular form for the convenience of clinicians. Specific treatment-planning considerations and biomechanics that can lead to an efficient and long-lasting result are also discussed. Conclusion: The need of the hour is an evidence-based approach, along with a well-designed prospective randomized control trial to understand the importance of each factor influencing these cases. Until that time, giving the available information in a simplified way can be a quality approach to these cases.

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 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.

TESTING STRICT STATIONARITY WITH APPLICATIONS TO MACROECONOMIC TIME SERIES

2017 ◽  
Vol 58 (4) ◽  
pp. 1227-1277 ◽  
Author(s):  
Yongmiao Hong ◽  
Xia Wang ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Macroeconomic Time Series ◽  
Strict Stationarity

DISTRIBUTIONS OF LOCAL MANDELBROT-HURST EXPONENTS: MOTOR ACTIVITY IN FETAL RATS OF COCAINIZED MOTHERS AND MANIC-DEPRESSIVE PATIENTS

Fractals ◽  
1995 ◽  
Vol 03 (04) ◽  
pp. 893-904 ◽  
Author(s):  
KAREN A. SELZ ◽  
ARNOLD J. MANDELL ◽  
CARL M. ANDERSON ◽  
WILLIAM P. SMOTHERMAN ◽  
MARTIN H. TEICHER
Keyword(s):  
Time Series ◽  
Motor Activity ◽  
Motor Behavior ◽  
Pregnant Rats ◽  
Behavioral Systems ◽  
Statistical Indices ◽  
Manic Depressive ◽  
The One ◽  
Depressive Patients ◽  
Hurst Exponents

Intermittency, in which the normalized weight of large fluctuations grows for increasingly longer statistical samples, is seen as irregular bursting activity in time and is characteristic of the behavior of many brain and behavioral systems. This pattern has been related to the brain-stabilizing interplay of the general mechanisms of silence-evoked sensitization and activity-evoked desensitization, which can be found at most levels of neurobiological function and which vary more smoothly and at much longer times than the phasic observables. We use both the global Mandelbrot-Hurst exponent and the distribution of local Mandelbrot-Hurst exponents, in combination with dynamical entropies, to quantitate the property of nonuniform persistence which we treat as both deterministically expansive and statistically diffusive. For example, varying the parameter of the one-dimensional, Manneville-Pomeau intermittency map generated time series which demonstrated systematic changes in these statistical indices of persistence. Relatively small doses of cocaine administered to pregnant rats increased statistical indices of expansiveness and persistence in fetal motor behavior. These techniques also model and characterize a breakdown of statistical scaling in 72-hour time series of the amount of motor activity in some hospitalized manic-depressive patients.

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