scholarly journals AUG-Segmenter: a user-friendly tool for segmentation of long time series

2010 ◽  
Vol 12 (3) ◽  
pp. 318-328 ◽  
Author(s):  
Abdullah Gedikli ◽  
Hafzullah Aksoy ◽  
N. Erdem Unal
Keyword(s):  
Time Series ◽  
Dynamic Programming ◽  
Linear Regression ◽  
Computer Program ◽  
Branch And Bound ◽  
Real World ◽  
The Third ◽  
Time Series Segmentation ◽  
Long Time ◽  
User Friendly

In this study, three algorithms are presented for time series segmentation. The first algorithm is based on the branch-and-bound approach, the second on the dynamic programming while the third is a modified version of the latter into which the remaining cost concept of the former is introduced. A user-friendly computer program called AUG-Segmenter is developed. Segmentation-by-constant and segmentation-by-linear-regression can be performed by the program. The program is tested on real-world time series of thousands of terms and found useful in performing segmentation satisfactorily and fast.

scholarly journals Multiscale behavior of a simple model for stock markets

2005 ◽  
Vol 2005 (2) ◽  
pp. 111-117
Author(s):  
Juan R. Sánchez
Keyword(s):  
Time Series ◽  
Simple Model ◽  
Power Law ◽  
Real World ◽  
Stock Markets ◽  
Time Range ◽  
Scaling Exponents ◽  
World Markets ◽  
Long Time ◽  
With Memory

The multiscale behavior of a recently reported model for stock markets is presented. It has been shown that indexes of real-world markets display absolute returns with memory properties on a long-time range, a phenomenon known as cluster volatility. The multiscale characteristics of an index are studied by analyzing the power-law scaling of the volatility correlations which display nonunique scaling exponents. Here such analysis is done on an artificial time series produced by a simple model for stock markets. After comparison, excellent agreements with the multiscale behavior of real-time series are found.

scholarly journals Mathematical Models in Insurance

1974 ◽  
Vol 7 (3) ◽  
pp. 192-202 ◽  
Author(s):  
Karl Borch
Keyword(s):  
Statistical Analysis ◽  
Mathematical Models ◽  
Poisson Distribution ◽  
Real World ◽  
Real Life ◽  
Recent Model ◽  
Risk Theory ◽  
Second Step ◽  
The Third ◽  
Long Time

This paper contains little which can be considered as new. It gives a survey of results which have been presented over the last 10-15 years. At one time these results seemed very promising, but in retrospect it is doubtful if they have fulfilled the expectations they raised. In this situation it may be useful to retrace one's steps and see if problems can be reformulated or if new approaches can be found.Mathematical models have been used in insurance for a long time. One of the first was the Gompertz mortality law; a more recent model, which has been intensively studied is the Compound Poisson Distribution in Lundberg's risk theory.When a model is introduced, one usually proceeds by stages. The first step is to see if the model appears acceptable on a priori reasons. If it does, the second step is to examine the implications of the model, to see if any of these are in obvious contradiction with observations. If the result of this examination is satisfactory, the third step is usually a statistical analysis to find out how well the model approximates the situation in real life, which one wants to analyse. If the model passes this second examination, the next and final step may be to estimate the parameters of the model, and use it in practice, i.e. to make decisions in the real world.The advantage of working with a model is that it gives an overall purpose to the collection and analysis of data. A good model should tell us which data we need, and why.

scholarly journals RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

2019 ◽  
Vol 33 ◽  
pp. 5409-5416 ◽  
Author(s):  
Qingsong Wen ◽  
Jingkun Gao ◽  
Xiaomin Song ◽  
Liang Sun ◽  
Huan Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Abrupt Change ◽  
Decomposition Algorithm ◽  
Real World Data ◽  
Regression Problem ◽  
Sparse Regularization ◽  
Least Absolute Deviations ◽  
Long Time ◽  
Non Local

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.

Alkohol im Tank – Bio-Kraftstoffe und die Grenzen moderner Mobilität

2004 ◽  
Vol 34 (136) ◽  
pp. 455-468
Author(s):  
Hartwig Berger
Keyword(s):  
Third World ◽  
Fossil Fuel ◽  
Energy Resources ◽  
The Third ◽  
Political Conflicts ◽  
The Future ◽  
Long Time ◽  
Food Market ◽  
The One ◽  
The Third World

The article discusses the future of mobility in the light of energy resources. Fossil fuel will not be available for a long time - not to mention its growing environmental and political conflicts. In analysing the potential of biofuel it is argued that the high demands of modern mobility can hardly be fulfilled in the future. Furthermore, the change into using biofuel will probably lead to increasing conflicts between the fuel market and the food market, as well as to conflicts with regional agricultural networks in the third world. Petrol imperialism might be replaced by bio imperialism. Therefore, mobility on a solar base pursues a double strategy of raising efficiency on the one hand and strongly reducing mobility itself on the other.

Comparative assessment of informational content of the equations of F.P. Hadlock and the computer program of V.N. Demidov in determination of term of gestation and fetal weight in the III trimester of gestation

2017 ◽  
Author(s):  
E.A. Derkach , O.I. Guseva
Keyword(s):  
Computer Program ◽  
High Precision ◽  
Gestational Age ◽  
Computer Programs ◽  
Comparative Assessment ◽  
Fetal Weight ◽  
Informational Content ◽  
Third Trimester ◽  
The Third

Objectives: to compare the accuracy of equations F.P. Hadlock and computer programs by V.N. Demidov in determining gestational age and fetal weight in the third trimester of gestation. Materials: 328 patients in terms 36–42 weeks of gestation are examined. Ultrasonography was performed in 0–5 days prior to childbirth. Results: it is established that the average mistake in determination of term of pregnancy when using the equation of F.P. Hadlock made 12,5 days, the computer program of V.N. Demidov – 4,4 days (distinction 2,8 times). The mistake within 4 days, when using the equation of F.P. Hadlock has met on average in 23,1 % of observations, the computer program of V.N. Demidov — 65,9 % (difference in 2,9 times). The mistake more than 10 days, took place respectively in 51,7 and 8,2 % (distinction by 6,3 times). At a comparative assessment of size of a mistake in determination of fetal mass it is established that when using the equation of F.P. Hadlock it has averaged 281,0 g, at application of the computer program of V.N. Demidov — 182,5 g (distinction of 54 %). The small mistake in the mass of a fetus which isn't exceeding 200 g at application of the equation of F.P. Hadlock has met in 48,1 % of cases and the computer program of V.N. Demidov — 64,0 % (distinction of 33,1 %). The mistake exceeding 500 g has been stated in 18 % (F.P. Hadlock) and 4,3 % (V.N. Demidov) respectively (distinction 4,2 times). Conclusions: the computer program of V.N. Demidov has high precision in determination of term of a gestation and mass of a fetus in the III pregnancy.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

Debt Markets and Investments

2019 ◽  
Keyword(s):  
Real World ◽  
Level Of Detail ◽  
Fixed Income ◽  
Key Concepts ◽  
Debt Markets ◽  
Basic Concepts ◽  
User Friendly ◽  
Distinguishing Features ◽  
Consistent Approach ◽  
Complex Subject

This book provides an objective look into the dynamic world of debt markets, products, valuation, and analysis. It also provides an in-depth understanding about this subject from experts in the field, both practitioners and academics. The coverage extends from discussing basic concepts and their application to increasingly intricate and real-world situations. This volume spans the gamut from theoretical to practical, while attempting to offer a useful balance of detailed and user-friendly coverage. The book has several distinguishing features. It blends the contributions of a global array of scholars and practitioners into a single review of some of the most important topics in this area. The book follows an internally consistent approach in format and style. Hence, it is collectively much more than a compilation of chapters from an array of different authors. It presents theory without unnecessary abstraction, quantitative techniques using basic bond mathematics, and conventions at a useful level of detail. It also incorporates how investment professionals analyze and manage fixed income portfolios. The book emphasizes empirical evidence involving debt securities and markets so it is understandable to a wide array of readers. Each chapter contains discussion questions to help reinforce key concepts. The end of the book contains guideline answers to each question. Readers interested in a broad survey will benefit as will those looking for more in-depth presentations of specific areas within this field of study. In summary, the book provides a fresh look at this intriguing and dynamic but often complex subject.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

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