scholarly journals S-ARMA model and Wold decomposition for covariance stationary interval-valued time series processes

2020 ◽  
Author(s):  
Jules Sadefo Kamdem ◽  
Babel Raissa Guemdjo Kamdem ◽  
Carlos Ougouyandjou
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Wold Decomposition ◽  
Stationary Interval ◽  
Interval Valued

AR and ARMA model order selection for time-series modeling with ImageNet classification

2021 ◽  
Vol 183 ◽  
pp. 108026
Author(s):  
Jihye Moon ◽  
Md Billal Hossain ◽  
Ki H. Chon
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Order Selection ◽  
Time Series Modeling ◽  
Model Order Selection ◽  
Model Order ◽  
Selection For

scholarly journals Set-valued and interval-valued stationary time series

2016 ◽  
Vol 145 ◽  
pp. 208-223 ◽  
Author(s):  
Xun Wang ◽  
Zhongzhan Zhang ◽  
Shoumei Li
Keyword(s):  
Time Series ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Interval Valued

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

scholarly journals Regime change analysis of interval-valued time series with an application to PM10

2015 ◽  
Vol 146 ◽  
pp. 337-346 ◽  
Author(s):  
Carmela Cappelli ◽  
Pierpaolo D'Urso ◽  
Francesca Di Iorio
Keyword(s):  
Time Series ◽  
Regime Change ◽  
Change Analysis ◽  
Interval Valued

The linguistic modeling of interval-valued time series: A perspective of granular computing

2019 ◽  
Vol 478 ◽  
pp. 476-498 ◽  
Author(s):  
Wei Lu ◽  
Wei Zhou ◽  
Dan Shan ◽  
Liyong Zhang ◽  
Jianhua Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Granular Computing ◽  
Linguistic Modeling ◽  
Interval Valued

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

2003 ◽  
Vol 11 (06) ◽  
pp. 671-690 ◽  
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Trading Volume ◽  
Arma Model ◽  
Future Trend ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
New Approach ◽  
Series Analysis ◽  
Closing Price

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

scholarly journals Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

2021 ◽  
Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Moving Average ◽  
Arma Model ◽  
Weather Conditions ◽  
Pv Systems ◽  
Auto Regressive ◽  
Micro Grid ◽  
One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

Time Series Models

2021 ◽  
pp. 255-270
Author(s):  
James Davidson
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Time Series Analysis ◽  
Poisson Process ◽  
Final Section ◽  
Reflection Principle ◽  
Time Series Models ◽  
Wold Decomposition ◽  
Linear Processes ◽  
Series Analysis

This chapter reviews some important ideas from time series analysis. The concepts of stationarity, independence, and exchangeability are defined and illustrated with examples. The Poisson process is examined in detail and then the class of linear processes, noting the implications of the Wold decomposition. The final section studies the random walk and the reflection principle.

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