Time series data are a collection of chronological observations which generated by several domains such as medical and financial fields. Over the years, different tasks classification, forecasting clustering have been proposed to analyze this type data. also used study effect interventions overtime. Moreover, in many fields science, learning causal structure dynamic systems time is considered a...

The aim of this paper is twofold. Firstly, we apply the model proposed by Jorion and Schwartz in their seminal article dated from 1986 in the Journal of Finance, and in this paper we test the integration of the European stock markets in the world market. Secondly, we apply the model using a recent high frequency data set, obtained after the single currency has been implemented. In this setting,...

Parameter constancy is a fundamental requirement for empirical models to be useful for forecasting, analysing economic policy, or testing economic theories. However, there are surprises in defining a constant-parameter model, such that models with time-varying coefficients, and expansion of the parameterization over time are both compatible with constancy, yet unbiased forecasts may not entail ...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

The Hodrick-Prescott (HP) filter has become a widely used tool for detrending integrated time series in applied econometric analysis. Even though the theoretical time series literature sums up an extensive catalogue of severe criticism against an econometric analysis of HP filtered data, the original Hodrick and Prescott (1980, 1997) suggestion to measure the strength of association between (ma...