scholarly journals Capacity Method of Rare Events Analysis in the Area of Services

2020 ◽  
pp. 132-142
Author(s):  
Yuriy Aleksandrovich Korablev ◽  
Polina Sergeevna Golovanova ◽  
Tatyana Andreevna Kostritsa
Keyword(s):  
Time Series ◽  
Service Sector ◽  
Rare Events ◽  
Real Data ◽  
Mathematical Methods ◽  
Future Period ◽  
Communication Services ◽  
Beauty Salon ◽  
The Moment ◽  
Capacity Method

Imagine that you are owner of some service. You need to determine for a certain future period the work plan of your craftsmen, the number of consumables needed. To do this, you need to make a forecast of future services number. Classical mathematical methods of working with time series are not suitable for this task. Aggregation of data on services by months and the compilation of a time series can only confuse. Forecasting services should be performed using methods designed to work with rare events. Rare events are devoted to relatively few works. Methods for the study of rare events are significantly less than methods for analyzing frequent events (time series). The most popular method of studying rare events at the moment is the use of the theory of random processes, which uses a stream of Poisson or Erlang events. However, using random streams, one cannot predict the very moment of the occurrence of an event. The paper describes an approach to the rare events analysis, which is based on: dividing events by identifiers of the sources in which they are formed; regression process parameters occurring within the sources, resulting in these events formation; search by any known method of parameters change patterns; the process start itself to obtain a forecast of the following events time occurrence. For the consumption processes and the disturbances growth process, which are the most common processes of the events formation in the economy, a method is proposed for restoring the consumption or accumulating disturbances rate from the rare events history. Services as can be modeled as the process of accumulating disturbances to a certain level. The article is devoted to the application of the capacity method of rare events analysis on real data in the service sector (haircut in a hairdresser, a manicure in a beauty salon, cellular communication services). The task is to restore the function that leads to the acquisition of services, and then predict the following events.

scholarly journals Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua
Keyword(s):  
Time Series ◽  
Default Risk ◽  
Real Life ◽  
Statistical Error ◽  
Real Data ◽  
Sampling Period ◽  
Calibration Procedure ◽  
Industry Standards ◽  
Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test

2021 ◽  
pp. 190-200
Author(s):  
Lesia Mochurad ◽  
Yaroslav Hladun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Real Data ◽  
Finger Tapping ◽  
Similar Distribution ◽  
Model Learning ◽  
Machine Learning Model ◽  
Finger Tapping Test

The paper considers the method for analysis of a psychophysical state of a person on psychomotor indicators – finger tapping test. The app for mobile phone that generalizes the classic tapping test is developed for experiments. Developed tool allows collecting samples and analyzing them like individual experiments and like dataset as a whole. The data based on statistical methods and optimization of hyperparameters is investigated for anomalies, and an algorithm for reducing their number is developed. The machine learning model is used to predict different features of the dataset. These experiments demonstrate the data structure obtained using finger tapping test. As a result, we gained knowledge of how to conduct experiments for better generalization of the model in future. A method for removing anomalies is developed and it can be used in further research to increase an accuracy of the model. Developed model is a multilayer recurrent neural network that works well with the classification of time series. Error of model learning on a synthetic dataset is 1.5% and on a real data from similar distribution is 5%.

scholarly journals Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers

Mathematics ◽  
2018 ◽  
Vol 6 (7) ◽  
pp. 124 ◽  
Author(s):  
Elena Barton ◽  
Basad Al-Sarray ◽  
Stéphane Chrétien ◽  
Kavya Jagan
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Data Analysis ◽  
Real Data ◽  
Real Data Analysis ◽  
Convex Optimisation ◽  
Dynamical Time

In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

scholarly journals Analysis of Dynamical Behavior for Epidemic Disease COVID-19 with Application

2021 ◽  
Vol 12 (4) ◽  
pp. 568-577
Author(s):  
Dr. Maysoon M. Aziz, Et. al.
Keyword(s):  
Time Series ◽  
Linear System ◽  
Power Spectrum ◽  
Dynamical Behavior ◽  
Simulated Data ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Disease ◽  
The World ◽  
Non Linear System

In this paper, we will use the differential equations of the SIR model as a non-linear system, by using the Runge-Kutta numerical method to calculate simulated values for known epidemiological diseases related to the time series including the epidemic disease COVID-19, to obtain hypothetical results and compare them with the dailyreal statisticals of the disease for counties of the world and to know the behavior of this disease through mathematical applications, in terms of stability as well as chaos in many applied methods. The simulated data was obtained by using Matlab programms, and compared between real data and simulated datd were well compatible and with a degree of closeness. we took the data for Italy as an application.  The results shows that this disease is unstable, dissipative and chaotic, and the Kcorr of it equal (0.9621), ,also the power spectrum system was used as an indicator to clarify the chaos of the disease, these proves that it is a spread,outbreaks,chaotic and epidemic disease .

scholarly journals FDI Time Series Forecasts: Evidence from Emerging Markets

2021 ◽  
Author(s):  
Marina Dobrota ◽  
Nikola Zornić ◽  
Aleksandar Marković
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Economic Development ◽  
Emerging Markets ◽  
Research Question ◽  
Southeastern Europe ◽  
Western Balkans ◽  
Future Period ◽  
Fdi Inflows ◽  
The Future

Research Question: This paper investigates the trend and flow of foreign direct investments (FDI) in emerging markets, with the focus on FDI in Serbia in comparison with akin countries from the region. Motivation: FDI is an important factor of growth and prosperity in developing countries. It largely influences trade, productivity, and economic development of a receiving country. Based on UNCTAD’s World Investment Report of 2019, the share of global FDI in developing countries was 54 per cent, which was a record. Recently, Serbia has been recognized as one of the most popular destinations for FDI in Southeastern Europe. This motivated us to analyze the chances and possibilities of enlargement of FDI in Serbia, as well in other Balkan countries. Idea: The main idea of the paper is to analyze and estimate time series of FDI net inflows for Serbia. We strive to investigate whether FDI demonstrates the durable growth in the future period of time. Furthermore, we compare the state of Serbian FDI with the former Yugoslav countries, in search for disparities or similarities. Data: We observed the FDI net inflows that are measured in current US dollars, while the data were retrieved from the World Bank database. The earliest available time point is 1992, while the latest available year of observation is 2018. Tools: We estimated the FDI net flow time series using a list of suitable ARIMA models, and we have chosen the best model fit among them using AIC and BIC criteria. Findings: We have found that Serbia and North Macedonia show a mild growth in future investments. A significant percentage of the cumulative FDI inflows from EU companies have been invested precisely in Serbia, while in North Macedonia, fostering FDI has been promoted as one of the main instruments for employment and economic development. Oher Yugoslav countries tend to stagnate in the future period, which is in literature called a negative ‘Western Balkans’ effect on FDI. Contribution: Findings of the mild growth in FDI inflows in Serbia and North Macedonia contribute to the policy of attracting the FDI inflows in the countries of Southeastern Europe.

Empirical analysis of time series using feature selection algorithms

2021 ◽  
Author(s):  
Mikhail Kanevski
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Feature Selection ◽  
Dimensional Space ◽  
Multivariate Time Series ◽  
Feature Space ◽  
Real Data ◽  
Theoretical Concepts ◽  
Wide Range ◽  
Linear And Nonlinear

<p>Nowadays a wide range of methods and tools to study and forecast time series is available. An important problem in forecasting concerns embedding of time series, i.e. construction of a high dimensional space where forecasting problem is considered as a regression task. There are several basic linear and nonlinear approaches of constructing such space by defining an optimal delay vector using different theoretical concepts. Another way is to consider this space as an input feature space – IFS, and to apply machine learning feature selection (FS) algorithms to optimize IFS according to the problem under study (analysis, modelling or forecasting). Such approach is an empirical one: it is based on data and depends on the FS algorithms applied. In machine learning features are generally classified as relevant, redundant and irrelevant. It gives a reach possibility to perform advanced multivariate time series exploration and development of interpretable predictive models.</p><p>Therefore, in the present research different FS algorithms are used to analyze fundamental properties of time series from empirical point of view. Linear and nonlinear simulated time series are studied in detail to understand the advantages and drawbacks of the proposed approach. Real data case studies deal with air pollution and wind speed times series. Preliminary results are quite promising and more research is in progress.</p>

Statistical-Mathematical Methods Used in Analysis of Trade in Industrial Products

2019 ◽  
Vol 34 ◽  
pp. 315-325
Author(s):  
Tomiță Constantin Vasile ◽  
Luminița Popescu ◽  
Cora Ionela Dăniasă ◽  
Anica Iancu ◽  
Virgil Popescu
Keyword(s):  
Dairy Products ◽  
Economic Importance ◽  
Mathematical Methods ◽  
Close Link ◽  
Human Needs ◽  
Commodity Production ◽  
The People ◽  
Long Time ◽  
Set Up ◽  
The Moment

Dairy products are of great socio-economic importance in Romania today. These products have both nutritional and economic importance. The market is the economic category of commodity production in which it expresses the totality of the sale-purchase acts viewed in an organic unit with the relations it generates and in connection with the space in which it takes place. The market originated a long time ago, being related to the moment when, in order to satisfy their existential needs, "discovered" and increasingly "conscious", the people exchanged between them, respectively collectivities, the surpluses held by each individual - individually or collectively. The exchange, set up as a means of realizing its own interests, has seen various forms and has evolved continuously, being still the foundation of all the economies of the world. The market has grown based on the amplification and diversification of human needs. The satisfaction of these needs is given by the close link between producers and consumers.

scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

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