Robust and nonparametric detection of shifts in time series
نویسنده
چکیده
A classical test for the detection of level shifts in such weakly dependent data is the CUSUM test, which compares the partial sum of the first m observations to the sum of all observations for each candidate change-point m, and maximizes this statistic with respect to m after some appropriate scaling. Asymptotical critical values for the CUSUM test can be calculated from tables of the Kolmogorov-Smirnov distribution, i.e. the distribution of the supremum of the Brownian bridge process.
منابع مشابه
A new adaptive exponential smoothing method for non-stationary time series with level shifts
Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...
متن کاملSpectral Estimation of Stationary Time Series: Recent Developments
Spectral analysis considers the problem of determining (the art of recovering) the spectral content (i.e., the distribution of power over frequency) of a stationary time series from a finite set of measurements, by means of either nonparametric or parametric techniques. This paper introduces the spectral analysis problem, motivates the definition of power spectral density functions, and reviews...
متن کاملA Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis
Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is...
متن کاملOn the Detection of Trends in Time Series of Functional Data
A sequence of functions (curves) collected over time is called a functional time series. Functional time series analysis is one of the popular research areas in which statistics from such data are frequently observed. The main purpose of the functional time series is to predict and describe random mechanisms that resulted in generating the data. To do so, it is needed to decompose functional ti...
متن کاملImproving the stability of the power system based on static synchronous series compensation equipped with robust model predictive control
Low-frequency oscillations (LFO) imperil the stability of the power system and reduce the Capacity of transmission lines. In the power systems, FACTS devices and Power System stabilizers are used to improve the stability. Static synchronous series compensators is one of the most important FACTS devices. This paper investigates the damping of LFO with static synchronous series compensator (SSSC)...
متن کامل