scholarly journals Devising a method for constructing the optimal model of time series forecasting based on the principles of competition

2021 ◽  
Vol 5 (4 (113)) ◽  
pp. 6-11
Author(s):  
Oksana Mulesa ◽  
Igor Povkhan ◽  
Tamara Radivilova ◽  
Oleksii Baranovskyi
Keyword(s):  
Time Series ◽  
Predictive Models ◽  
Forecast Error ◽  
Optimal Model ◽  
Absolute Value ◽  
Forecasting Methods ◽  
Optimal Values ◽  
Weight Coefficients ◽  
Forecast Quality ◽  
Selection Of

This paper reports the analysis of a forecasting problem based on time series. It is noted that the forecasting stage itself is preceded by the stages of selection of forecasting methods, determining the criterion for the forecast quality, and setting the optimal prehistory step. As one of the criteria for a forecast quality, its volatility has been considered. Improving the volatility of the forecast could ensure a decrease in the absolute value of the deviation of forecast values from actual data. Such a criterion should be used in forecasting in medicine and other socially important sectors. To implement the principle of competition between forecasting methods, it is proposed to categorize them based on the values of deviations in the forecast results from the exact values of the elements of the time series. The concept of dominance among forecasting methods has been introduced; rules for the selection of dominant and accurate enough predictive models have been defined. Applying the devised rules could make it possible, at the preceding stages of the analysis of the time series, to reject in advance the models that would surely fail from the list of predictive models available to use. In accordance with the devised method, after applying those rules, a system of functions is built. The functions differ in the sets of predictive models whose forecasting results are taken into consideration. Variables in the functions are the weight coefficients with which predictive models are included. Optimal values for the variables, as well as the optimal model, are selected as a result of minimizing the functions built. The devised method was experimentally verified. It has been shown that the constructed method made it possible to reduce the forecast error from 0.477 and 0.427 for basic models to 0.395 and to improve the volatility of the forecast from 1969.489 and 1974.002 to 1607.065

scholarly journals Trend Models for Analysis of Socio-Economic Security

2020 ◽  
Vol 19 (2) ◽  
pp. 108-112
Author(s):  
D. N. Shvaiba
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Economic Security ◽  
Practical Significance ◽  
Mean Square ◽  
Proposed Model ◽  
Upper Limits ◽  
Selection For ◽  
Random Nature ◽  
Selection Of

Correctness of the trend selection for predicting characteristics of socio-economic security statistics can be qualified with the help of a mean square error value and an aspect of “Ascending” and “Descending” series (although there are other aspects, for example, the aspects based on the median of a sample). According to the proposed model, it is possible to predetermine average monitoring errors for development of lower and upper limits of the forecast version in respect of values for characteristics of socio-economic security statistics. Model creation is a labor-intensive process, so that when predicting  characteristics of socio-economic security statistics, it is advisable to use, as a rule, a deterministic component of trend models. At the same time, an assumption about random nature of deviations in empirical values of time series from a trend for 5 %  significance  value is not  rejected.  Study of  the material allows us to admit that it is impossible  to  note exact cycles in time series of values for characteristics of  socio-economic  security  statistics.  However,  this does not represent a basis for the conclusion about presence of cycles in time series of values for characteristics of socio-economic security statistics because these cycles do not coincide in time, there is no clear priority in exceedance of actual values for characteristics of socio-economic security statistics over the calculated ones obtained with the help of models, or, on the contrary, exceedance of the calculated values over the actual ones. Various approaches can be used to calculate a magnitude of the forecast error. Thus, a question pertaining to selection of trend models for an analysis of socio-economic security is natural due to difference in reliability of data when using different models, and correctness of the selection will improve an efficiency of the analysis. So the study acquires practical significance for economic entities and entire industries

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

MODEL OF SOCIAL POTENTIAL OF CHILDHOOD IN THE RUSSIAN REGIONS: THE RESULTS OF EXPERIMENTS

2019 ◽  
pp. 152-162
Author(s):  
A.G. Filipova ◽  
A.V. Vysotskaya
Keyword(s):  
Third Wave ◽  
Russian Regions ◽  
The Third ◽  
Control Factors ◽  
Social Potential ◽  
System Input ◽  
Optimal Values ◽  
Fixed Ideal ◽  
And Control ◽  
Selection Of

The article presents the results of mathematical experiments with the system «Social potential of childhood in the Russian regions». In the structure of system divided into three subsystems – the «Reproduction of children in the region», «Children’s health» and «Education of children», for each defined its target factor (output parameter). The groups of infrastructure factors (education, health, culture and sport, transport), socio-economic, territorial-settlement, demographic and en-vironmental factors are designated as the factors that control the system (input parameters). The aim of the study is to build a model îf «Social potential of childhood in the Russian regions», as well as to conduct experiments to find the optimal ratio of the values of target and control factors. Three waves of experiments were conducted. The first wave is related to the analysis of the dynam-ics of indicators for 6 years. The second – with the selection of optimal values of control factors at fixed ideal values of target factors. The third wave allowed us to calculate the values of the target factors based on the selected optimal values of the control factors of the previous wave.

scholarly journals Asymptotic Expansions and Strategies in the Online Increasing Subsequence Problem

2021 ◽  
Vol 174 (1) ◽  
Author(s):  
Amirlan Seksenbayev
Keyword(s):  
Dynamic Programming ◽  
Asymptotic Expansions ◽  
Dual Problem ◽  
Infinite Length ◽  
Expected Time ◽  
Selection Strategies ◽  
Dynamic Programming Equation ◽  
Optimal Values ◽  
Design Selection ◽  
Selection Of

AbstractWe study two closely related problems in the online selection of increasing subsequence. In the first problem, introduced by Samuels and Steele (Ann. Probab. 9(6):937–947, 1981), the objective is to maximise the length of a subsequence selected by a nonanticipating strategy from a random sample of given size $n$ n . In the dual problem, recently studied by Arlotto et al. (Random Struct. Algorithms 49:235–252, 2016), the objective is to minimise the expected time needed to choose an increasing subsequence of given length $k$ k from a sequence of infinite length. Developing a method based on the monotonicity of the dynamic programming equation, we derive the two-term asymptotic expansions for the optimal values, with $O(1)$ O ( 1 ) remainder in the first problem and $O(k)$ O ( k ) in the second. Settling a conjecture in Arlotto et al. (Random Struct. Algorithms 52:41–53, 2018), we also design selection strategies to achieve optimality within these bounds, that are, in a sense, best possible.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

Selection of Neural Network for Crime Time Series Prediction by Virtual Leave-One-Out Tests

2019 ◽  
pp. 117-133
Author(s):  
Stanisław Jankowski ◽  
Zbigniew Szymański ◽  
Zbigniew Wawrzyniak ◽  
Paweł Cichosz ◽  
Eliza Szczechla ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Prediction ◽  
Virtual Leave One Out ◽  
Leave One Out ◽  
Selection Of

scholarly journals Forecasting emergency department presentations

2007 ◽  
Vol 31 (1) ◽  
pp. 83 ◽  
Author(s):  
Robert Champion ◽  
Leigh D Kinsman ◽  
Geraldine A Lee ◽  
Kevin A Masman ◽  
Elizabeth A May ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Software Package ◽  
Exponential Smoothing ◽  
Statistical Forecasting ◽  
Forecasting Methods ◽  
Number Of Patients ◽  
Forecasting Performance ◽  
Optimal Forecasting

Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box?Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.

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