In this paper we build a Markov-Switching Autoregressive model to describe a long time series of wind speed measurement. It is shown that the proposed model is able to describe the main characteristics of this time series, and in particular the various time scales which can be observed in the dynamics, from daily to interannual fluctuations.

A method for an evaluation of the error between an unknown parameter and its estimator is developed. Its application enables us to preserve the asymptotic power of a constructed test. Testing problems in AR(1) and ARCH models are studied with a derivation of the asymptotic power function. Also the results are extended to AR(m) time series model.

The paper discusses some model related issues for time series of the number of shareholders in a stock. The point of departure is an integer-valued autoregressive model of order one. Empirical results are presented for some frequently traded stocks on the Finnish and Swedish stock markets. In these stock markets public records of the number of owners are reported monthly (Finland) and quarterly...

The purpose of this document is to create a data model and its serialization for expressing generic time series data. Already existing IVOA data models are reused as much as possible. The model is also made as generic as possible to be open to new extensions but at the same time closed for modifications. This enables maintaining interoperability throughout different versions of the data model. ...

Utilizing a temperature time-series prediction model to achieve good results can help us accurately sense the changes occurring in levels advance, which is important for human life. However, random fluctuations time series reduce accuracy of model. Decomposing data prior performing effectively influence and consequently improve results. In present study, we propose that combines seasonal-trend ...

time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. forecasting accuracy is one of the most important features of forecasting models. nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to ...

In this work, the time series modeling was used to predict the Tazareh coal mine risks. For this purpose, initially, a monthly analysis of the risk constituents including frequency index and incidence severity index was performed. Next, a monthly time series diagram related to each one of these indices was for a nine year period of time from 2005 to 2013. After extrusion of the trend, seasonali...

introduction: accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. water has a special place among the basic human needs, because it not hampers human life. the importance of the i...