scholarly journals Modelling and forecasting of quasi-periodic processes in technical objects based on cylindrical image models

2019 ◽  
pp. 387-393
Author(s):  
V R Krasheninnikov ◽  
Yu E Kuvayskova
Keyword(s):  
Time Series ◽  
Technical Condition ◽  
The State ◽  
Effective Management ◽  
Hydraulic Unit ◽  
Forecasting Models ◽  
Periodic Processes ◽  
Image Models ◽  
Accuracy Of Prediction ◽  
Modelling And Forecasting

Accurate forecasting of the state of technical objects is necessary for effective management. The technical condition of the object is characterized by a system of time series of monitored indicators. The time series often have difficultly predictable irregular periodicity (quasi-periodicity). In this paper, to improve the accuracy of such series forecasting, models of quasi-periodic processes in the form of samples of a cylindrical image are used. The application of these models is demonstrated by forecasting of a hydraulic unit vibrations. It is shown that the use of these models provides a higher accuracy of prediction compared with the classical approaches.

scholarly journals Modelling and forecasting of potato sales prices in Ukraine

2021 ◽  
Vol 7 (4) ◽  
pp. 160-179
Author(s):  
Inna Koblianska ◽  
Larysa Kalachevska ◽  
Stanisław Minta ◽  
Nataliia Strochenko ◽  
Svitlana Lukash
Keyword(s):  
Time Series ◽  
State Space ◽  
Security Policy ◽  
Exponential Smoothing ◽  
Food Prices ◽  
Information Criteria ◽  
The State ◽  
Agricultural Enterprises ◽  
Use Of Resources ◽  
Modelling And Forecasting

Purpose. Under the background of the climate change and other crises, the world food system is becoming increasingly vulnerable to price fluctuations. This highlights the need to consider and better manage the risks associated with price volatility in accordance with the principles of a market economy and simultaneously protecting the most vulnerable groups of population. Responding to these challenges, in this study we aim to determine the main parameters of time series of potato sales prices in agricultural enterprises in Ukraine, to build an appropriate model, and to form a short-term (one-year) forecast. Methodology / approach. We used in the research the data from the State Statistics Service of Ukraine on average monthly sales prices of potatoes in agricultural enterprises from December 2012 to July 2021 (104 observations) adjusted for the price index of crop products sold by enterprises for the month (with December 2012 base period). Decomposition was used to determine the characteristics of the time series; exponential smoothing methods (Holt-Winters and State Space Framework – ETS) and autoregressive-moving average were used to find the model that fits the actual data the best and has high prognostic quality. We applied the Rstudio forecast package to model and to forecast the time series. Results. The time series of potato sales prices in enterprises is characterized by seasonality (mainly related to seasonal production) with the lowest prices in November, and the highest – in June; although, other periods of price growth were identified during the year: in January and April. The ARMA (2, 2) (1,0)12 with a non-zero mean was found to be the best model for forecasting potatoes sales prices. ARMA (2, 2) (1,0)12, compared to the state-space exponential smoothing model with additive errors – ETS (A), better fits the observed data and provides more accurate forecasting model (with lower errors). Forecast made with ARMA (2, 2) (1,0)12 shows that potato sale prices in agricultural enterprises in November 2021 (months with the lowest price) will range from 2154.76 UAH/t to 7414.57 UAH/t, in June 2022 – from 3016.72 UAH/t to 14051.63 UAH/t (prices of July 2021) with a probability of 95%. The forecast’s mean absolute percentage error is 14.87%. Originality / scientific novelty. This research deepens the methodological basis for food prices modelling and forecasting, thus contributing to the agricultural economics science development. The obtained results confirm the previous research findings on the better quality of food prices forecasts made with autoregressive models (for univariate time series) compared with exponential smoothing. Additionally, the study reveals advantages of the state space framework for exponential smoothing (ETS) compared to Holt-Winters methods in case of time series with seasonality: although the ETS model overlaps with the observed (train) data, it is better in terms of information criteria and forecasting (for the test data). Practical value / implications. The obtained results can serve as an information basis for decision-making on potato production and sales by producers, on more efficient use of resources by the population, on more effective measures to support industrial potato growing, to implement social programs and food security policy by the government.

scholarly journals Tourism Demand Modelling and Forecasting: A Review of Literature

2021 ◽  
Vol 10(4) (10(4)) ◽  
pp. 1370-1393
Author(s):  
Musonera Abdou ◽  
Edouard Musabanganji ◽  
Herman Musahara
Keyword(s):  
Time Series ◽  
Spatial Regression ◽  
Demand Forecasting ◽  
Tourism Demand ◽  
New Developments ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Modelling And Forecasting ◽  
Forecasting Techniques

This research examines 145 key papers from 1979 to 2020 in order to gain a better sense of how tourism demand forecasting techniques have changed over time. The three types of forecasting models are econometric, time series, and artificial intelligence (AI) models. Econometric and time series models that were already popular in 2005 maintained their popularity, and were increasingly used as benchmark models for forecasting performance assessment and comparison with new models. In the last decade, AI models have advanced at an incredible rate, with hybrid AI models emerging as a new trend. In addition, some new developments in the three categories of models, such as mixed frequency, spatial regression, and combination and hybrid models have been introduced. The main conclusions drawn from historical comparisons forecasting methods are that forecasting models have become more diverse, that these models have been merged, and that forecasting accuracy has improved. Given the complexities of predicting tourism demand, there is no single approach that works well in all circumstances, and forecasting techniques are still evolving.

scholarly journals Data-based mechanistic modelling and forecasting of hydrological systems

2000 ◽  
Vol 2 (1) ◽  
pp. 15-34 ◽  
Author(s):  
Matthew J. Lees
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Nonlinear Time Series ◽  
Flood Routing ◽  
Rainfall Runoff ◽  
Important Conclusion ◽  
Mechanistic Modelling ◽  
Forecasting Models ◽  
Common Time ◽  
Modelling And Forecasting

The paper presents a data-driven approach to the modelling and forecasting of hydrological systems based on nonlinear time-series analysis. Time varying parameters are estimated using a combined Kalman filter and fixed-interval-smoother, and state-dependent parameter relations are identified leading to nonlinear extensions to common time-series models such as the autoregressive exogenous (ARX) and general transfer function (TF). This nonlinear time-series technique is used as part of a data-based mechanistic modelling methodology where models are objectively identified from the data, but are only accepted as a reasonable representation of the system if they have a valid mechanistic interpretation. To this end it is shown that the TF model can represent a general linear storage model that subsumes many common hydrological flow forecasting models, and that the rainfall-runoff process can be represented using a nonlinear input transformation in combination with a TF model. One advantage of the forecasting models produced is that the Kalman filter can be used for real-time state updating leading to improved forecasts and an estimate of associated forecast uncertainty. Rainfall-runoff and flood routing case studies are included to demonstrate the power of the modelling and forecasting methods. One important conclusion is that optimal system identification techniques are required to objectively identify parallel flow pathways.

scholarly journals PREDICTING THE TECHNICAL CONDITION OF AN ELECTRIC SUBMERSIBLE PUMP BASED ON NEURAL NETWORK MODELING

2020 ◽  
Vol 20 (4) ◽  
pp. 37-46
Author(s):  
I.V. Karakulov ◽  
◽  
A.V. Kluiev ◽  
V.Yu. Stolbov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Initial Data ◽  
Monitoring System ◽  
Technical Condition ◽  
The State ◽  
Equipment Failure ◽  
Submersible Pump ◽  
Electric Submersible Pump

The problem of predicting the state of an Electric Submersible Pump during operation is considered. Downtime and shortages caused by pump failure lead to losses in oil pro-duction and require time to replace equipment. By predicting the condition of the equipment, it is possible to minimize pump maintenance costs and reduce well downtime. Expert systems and pre-dictive analytics methods are used to analyze the state of systems. The scientific work uses methods that are based on artificial neural networks. Purpose of research. Elaboration of the issues of fore-casting the technical condition of the pump through by using machine-learning models. Materials and methods. Equipment failure forecasting is carried out using time series analysis. The data was obtained from telemetric sensors of the monitoring system installed on an electric submersible pump. The initial data were taken at one-minute intervals. Initial data preprocessing was carried out. The data was cleared of values (peaks) that are clearly got out of normal operation and places where the phase voltage was equal to zero were removed. An artificial neural network with the LSTM neuron type is used to predict time series. Time series forecasting was carried out for five days. Evaluating system parameters over long periods allows you to assess the condition of its compo-nents and prevent equipment failure. Results. The possibilities of neural networks trained on the ba-sis of data from telemetric sensors of the monitoring system for predicting the values of vertical vi-bration of the pump are investigated. The use of a neural network model in the form of LSTM, which has shown good results in the analysis of time series, is justified. It was found that neural net-works capture the trend well within the time series, which indicates the possibility of using it together with the expert system. Conclusion. The proposed methods and models are tested on real data, which confirms the possibility of their use in the development of an intelligent information system for managing the technical condition of an Electric Submersible Pump during operation.

Method of tribodiagnostics of d-30KU engines/KP with the use of a microwave plasma complex: results of metrological examination and analysis of the accuracy

2020 ◽  
pp. 22-29
Author(s):  
A. Bogoyavlenskiy ◽  
A. Bokov
Keyword(s):  
Gas Turbine ◽  
Microwave Plasma ◽  
Technical Condition ◽  
The State ◽  
Turbine Engines ◽  
Metrological Examination ◽  
Gas Turbine Engines ◽  
High Frequency Plasma ◽  
Frequency Plasma ◽  
Primary Standards

The article contains the results of the metrological examination and research of the accuracy indicators of a method for diagnosing aircraft gas turbine engines of the D30KU/KP family using an ultra-high-frequency plasma complex. The results of metrological examination of a complete set of regulatory documents related to the diagnostic methodology, and an analysis of the state of metrological support are provided as well. During the metrological examination, the traceability of a measuring instrument (diagnostics) – an ultrahigh-frequency plasma complex – is evaluated based on the scintillation analyzer SAM-DT-01–2. To achieve that, local verification schemes from the state primary standards of the corresponding types of measurements were built. The implementation of measures to eliminate inconsistencies identified during metrological examination allows to reduce to an acceptable level the metrological risks of adverse situations when carrying out aviation activities in industry and air transportation. In addition, the probability of occurrence of errors of the first and second kind in the technological processes of tribodiagnostics of aviation gas turbine engines is reduced when implementing a method that has passed metrological examination in real practice. At the same time, the error in determining ratings and wear indicators provides acceptable accuracy indicators and sufficient reliability in assessing the technical condition of friction units of the D-30KP/KP2/KU/KU-154 aircraft engines.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

scholarly journals Vitamin D, SARS-CoV-2 and Causal Associations in Transversal Studies: The Time-Series Analysis to Reveal Potential Confounders. Comment on Gaudio et al. Vitamin D Levels Are Reduced at the Time of Hospital Admission in Sicilian SARS-CoV-2-Positive Patients. Int. J. Environ. Res. Public Health 2021, 18, 3491

2021 ◽  
Vol 18 (13) ◽  
pp. 6793
Author(s):  
Cristiano Ialongo ◽  
Antonella Farina ◽  
Raffaella Labriola ◽  
Antonio Angeloni ◽  
Emanuela Anastasi
Keyword(s):  
Public Health ◽  
Vitamin D ◽  
Time Series ◽  
Severe Acute Respiratory Syndrome ◽  
Time Series Analysis ◽  
Hospital Admission ◽  
The State ◽  
Series Analysis ◽  
Vitamin D Levels ◽  
Time Of Admission

We read with great interest the paper by Gaudio and colleagues on vitamin D and on the state of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the time of admission [...]

scholarly journals Phase Synchronization Is the Amplified Result by the Hilbert Transform

2015 ◽  
Vol 2015 ◽  
pp. 1-3 ◽  
Author(s):  
Ming-Chi Lu ◽  
Hsing-Chung Ho ◽  
Chen-An Chan ◽  
Chia-Ju Liu ◽  
Jiann-Shing Lih ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Hilbert Transform ◽  
Phase Synchronization ◽  
Nonlinear Dynamical Systems ◽  
The State ◽  
Nonlinear Dynamical ◽  
Large Correlation ◽  
Current Usage ◽  
The Hilbert Transform

We investigate the interplay between phase synchronization and amplitude synchronization in nonlinear dynamical systems. It is numerically found that phase synchronization intends to be established earlier than amplitude synchronization. Nevertheless, amplitude synchronization (or the state with large correlation between the amplitudes) is crucial for the maintenance of a high correlation between two time series. A breakdown of high correlation in amplitudes will lead to a desynchronization of two time series. It is shown that these unique features are caused essentially by the Hilbert transform. This leads to a deep concern and criticism on the current usage of phase synchronization.

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