scholarly journals Generalized Relational Tensors For Irregularly Sampled Time Series: Methods To Store, Re-generate, And Predict

2021 ◽  
Author(s):  
Vasilii Gromov ◽  
Anastasia Necheporenko ◽  
Andrei Gaisin ◽  
Ilya Volkov ◽  
Stanislav Diner
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Chaotic Time Series ◽  
Optimization Techniques ◽  
Initial Time ◽  
Auto Correlation ◽  
Initial Time Series ◽  
Auto Correlation Function ◽  
Estimate Quality

Abstract The paper deals with a generalized relational tensor, a novel discrete structure to store information about a time series, and algorithms (1) to fill the structure, (2) to generate a time series from the structure, and (3) to predict a time series, for both regularly and irregularly sampled time series. The algorithms combine the concept of generalized z-vectors with ant colony optimization techniques. In order to estimate quality of the storing/re-generating procedure, a difference between characteristics of the initial and regenerated time series is used. The structure allows working with a multivariate time series, with an irregularly sampled time series, and with a number of series as well. For chaotic time series, a difference between characteristics of the initial time series (the highest Lyapunov exponent, the auto-correlation function) and those of the time series re-generated from a structure is used to assess the effectiveness of the algorithms in question. The approach has shown fairly good results for periodic and benchmark chaotic time series and satisfactory results for real-world chaotic data.

scholarly journals A forecasting the consumer price index using time series model

2021 ◽  
Vol 107 ◽  
pp. 10002
Author(s):  
Volodymyr Shinkarenko ◽  
Alexey Hostryk ◽  
Larysa Shynkarenko ◽  
Leonid Dolinskyi
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Initial Time ◽  
Autocorrelation Functions ◽  
Minimum Error ◽  
Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

scholarly journals Forecasting The Exchange Rate Between Euro And USD: Probabilistic Approach Versus ARIMA And Exponential Smoothing Techniques

2012 ◽  
Vol 28 (2) ◽  
pp. 171 ◽  
Author(s):  
Paraschos Maniatis
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Probabilistic Approach ◽  
Laplace Distribution ◽  
Exponential Smoothing ◽  
Initial Time ◽  
Font Size ◽  
Smoothing Techniques ◽  
Initial Time Series ◽  
The Exchange Rate

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNoSpacing"><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-themecolor: text1; mso-ansi-language: EN-US;">This study attempts to model the exchange rate between Euro and USD using univariate models- in particular ARIMA and exponential smoothing techniques. The time series analysis reveals non stationarity in data and, therefore, the models fail to give reliable predictions. However, differencing the initial time series the resulting series shows strong resemblance to white noise. The analysis of this series advocates independence in data and distribution satisfactorily close to Laplace distribution. The application of Laplace distribution offers reliable probabilities in forecasting changes in the exchange rate.</span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

Research on a Method of Fault Signal Extraction Based on Improved Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 2692-2696
Author(s):  
Ying Wang ◽  
You Rong Li ◽  
Xiao Qin Zhu ◽  
Pan Lin ◽  
Yue Sheng Luo
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Partial Information ◽  
Initial Time ◽  
Time Signal ◽  
Shock Attenuation ◽  
Information Time ◽  
Initial Time Series ◽  
Fault Information ◽  
Sequential Decomposition

Considering the difficulty of diagnosis signal de-noising and feature extraction problems, according to the characteristics of periodicity and shock attenuation respond of mechanical fault vibration signals, a method of improved sequential decomposition algorithm is proposed, it transforms an initial time series into a group of two-dimensional time series, prominent time series partial information, time series decomposition is reversible, can be used for filtering and feature extraction of time signal. Through the simulation and experiments, the validity of method for highlighting partial feature information of the signal is verified, helping to extract weak fault information in strong background noise environment.

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

Dynamics of the cardiovascular autonomic regulation during orthostatic challenge is more relaxed in women

2018 ◽  
Vol 63 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Sina Reulecke ◽  
Sonia Charleston-Villalobos ◽  
Andreas Voss ◽  
Ramón González-Camarena ◽  
Jesús González-Hermosillo ◽  
...  
Keyword(s):  
Time Series ◽  
Sympathetic Activity ◽  
Low Frequency ◽  
Short Term ◽  
Auto Correlation ◽  
Complete Test ◽  
Auto Correlation Function ◽  
Head Up Tilt ◽  
Cardiovascular Autonomic Regulation ◽  
Sustained Increase

AbstractLinear dynamic analysis of cardiovascular and respiratory time series was performed in healthy subjects with respect to gender by shifted short-term segments throughout a head-up tilt (HUT) test. Beat-to-beat intervals (BBI), systolic (SYS) and diastolic (DIA) blood pressure and respiratory interval (RESP) time series were acquired in 14 men and 15 women. In time domain (TD), the descending slope of the auto-correlation function (ACF) (BBI_a31cor) was more pronounced in women than in men (p<0.05) during the HUT test and considerably steeper (p<0.01) at the end of orthostatic phase (OP). The index SYS_meanNN was slightly but significantly lower (p<0.05) in women during the complete test, while higher respiratory frequency and variability (RESP_sdNN) were found in women (p<0.05), during 10–20 min after tilt-up. In frequency domain (FD), during baseline (BL), BBI-normalized low frequency (BBI_LFN) and BBI_LF/HF were slightly but significantly lower (p<0.05), while normalized high frequency (BBI_HFN) was significantly higher in women. These differences were highly significant from the first 5 min after tilt-up (p<0.01) and highly significant (p<0.001) during 10–14 min of OP. Findings revealed that men showed instantaneously a pronounced and sustained increase in sympathetic activity to compensate orthostatism. In women, sympathetic activity was just increased slightly with delayed onset without considerably affecting sympatho-vagal balance.

scholarly journals FORECASTING THE GROWTH RATE OF GULLIES IN THE UDMURT REPUBLIC

2019 ◽  
Vol 29 (3) ◽  
pp. 383-393
Author(s):  
M.Yu. Zaitseva ◽  
I.I. Rysin
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Linear Growth ◽  
Extreme Values ◽  
Initial Time ◽  
Linear Growth Rate ◽  
Medium Term ◽  
Term Forecast ◽  
Initial Time Series ◽  
Udmurt Republic

The present study is aimed at forecasting the processes of gullyerosion in the Udmurt Republic using the methods of mathematical modeling. Five time series characterizing the average linear growth rate of gullies for the period from 1978 to 2017 were selected as a source material. Gullies were grouped according to the geographical principle and genesis. As part of this work, it is expected to build a medium-term forecast for the period 2018-2022. Fourier analysis was chosen as the basis for working with the initial time series. The results of the obtained models are graphically displayed. Subsequent regression analysis confirmed the validity of the model for at least four of the five groups of gullies. However, when comparing the obtained forecast values with those actually measured in 2018, it turned out that this model could not take into account the possible extreme values of the growth of individual gullies in the group.

scholarly journals Usage of time series models in assessing the value of demand in the insurance market

2019 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Nermin Saad Mohamed Fahmy ◽  
Sharifah Alrajhi
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Financial Institutions ◽  
Insurance Market ◽  
Time Series Models ◽  
Moving Averages ◽  
Quarterly Data ◽  
Auto Correlation ◽  
Auto Correlation Function ◽  
Auto Regression

Insurance can affect savings and investment as insurance and reinsurance companies are among the most important financial institutions in the country's economy. The researcher will analyze, based on the partial auto correlation function and experimenting with several models of auto regression and moving averages, and testing the most appropriate model to describe the quarterly data on the number of polices.

scholarly journals Forecasting the Romanian Unemployment Rate in Time of Health Crisis—A Univariate vs. Multivariate Time Series Approach

2021 ◽  
Vol 18 (21) ◽  
pp. 11165
Author(s):  
Adriana AnaMaria Davidescu ◽  
Simona-Andreea Apostu ◽  
Aurel Marin
Keyword(s):  
Time Series ◽  
Labor Market ◽  
Unemployment Rate ◽  
Social Protection ◽  
Multivariate Time Series ◽  
Healthcare Sector ◽  
Time Series Models ◽  
Market Outcomes ◽  
Health Crisis

Economic crises cause significant shortages in disposable income and a sharp decline in the living conditions, affecting healthcare sector, hitting the profitability and sustainability of companies leading to raises in unemployment. At micro level, these sharp decreases in earnings associated with unemployment and furthermore with the lack of social protection will impact the quality of life and finally the health of individuals. In time of crisis, it becomes vital to support not only the critical sectors of the economy, the assets, technology, and infrastructure, but to protect jobs and workers. This health crisis has hit hard the jobs dynamics through unemployment and underemployment, the quality of work (through wages, or access to social protection), and through the effects on specific groups, with a higher degree of vulnerability to unfavorable labor market outcomes. In this context, providing forecasts as recent as possible for the unemployment rate, a core indicator of the Romanian labor market that could include the effects of the market shocks it becomes fundamental. Thus, the paper aims to offer valuable forecasts for the Romanian unemployment rate using univariate vs. multivariate time series models for the period 2021–2022, highlighting the main patterns of evolution. Based on the univariate time series models, the paper predict the future values of unemployment rate based on its own past using self-forecasting and implementing ARFIMA and SETAR models using monthly data for the period January 2000–April 2021. From the perspective of multivariate time series models, the paper uses VAR/VECM models, analyzing the temporal interdependencies between variables using quarterly data for the period 2000Q1–2020Q4. The empirical results pointed out that both SETAR and VECM provide very similar results in terms of accuracy replicating very well the pre-pandemic period, 2018Q2–2020Q1, reaching the value of 4.1% at the beginning of 2020, with a decreasing trend reaching the value of 3.9%, respectively, 3.6% at the end of 2022.

scholarly journals TRAFFIC COLLISION TIME SERIES ANALYSIS (A CASE STUDY OF KARAJ–QAZVIN FREEWAY)

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 941-948 ◽  
Author(s):  
R. Sanayei ◽  
A. R. Vafainejad ◽  
J. Karami ◽  
H. Aghamohammadi
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Prediction Models ◽  
Ar Model ◽  
Periodic Pattern ◽  
Accident Prediction ◽  
Auto Correlation ◽  
Accident Data ◽  
Auto Correlation Function ◽  
Simulation Results

Abstract. The application of Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) in recent years has been improved in analyzing big traffic data, modelling traffic collisions and decreasing processing time in finding collision patterns. Accident prediction models for short and long time can help in designing and programming traffic plans and decreasing road accidents. Based on the above details, in this paper, the Karaj-Qazvin highway accident data (1097 samples) and its patterns from 2009 to 2013 have been analyzed using time series methods.In the first step, using auto correlation function (ACF) and partial auto correlation function (PACF), the rank of time series model supposed to be autoregressive (AR) model and in the second stage, its coefficients were found. In order to extract the accident data, ArcGIS software was run. Furthermore, MATLAB software was used to find the model rank and its coefficients. In addition, Stata SE software was used for statistical analysis. The simulation results showed that on the weekly scale, based on the trend and periodic pattern of data, the model type and rank, ACF and PACF values, an accurate weekly hybrid model (time series and PACF) of an accident can be created. Based on simulation results, the investigated model predicts the number of accident using two prior week data with the Root Mean Square Error (RMSE) equal to three.

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