Generalized subsampling procedure for non-stationary time series
نویسندگان
چکیده
منابع مشابه
Using Wavelets and Splines to Forecast Non-Stationary Time Series
This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...
متن کاملA 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 کاملStationary and non-stationary time series
Time series analysis is about the study of data collected through time. The field of time series is a vast one that pervades many areas of science and engineering particularly statistics and signal processing: this short article can only be an advertisement. Hence, the first thing to say is that there are several excellent texts on time series analysis. Most statistical books concentrate on sta...
متن کاملResampling and Subsampling for Financial Time Series
where {Pt, t = 0, 1, 2, . . .} is the price of a financial asset observed at time t, t can be measured in seconds, minutes, hours, days, etc. Standard examples for Pt are prices of company-shares quoted at major stock exchanges, interest rates and foreign exchange rates among different currencies. A Taylor series argument shows that Rt is close to the relative returns (Pt−Pt−1)/Pt−1 which are f...
متن کاملIntroduction to Non - Stationary Time Series
Consider a univariate time series {yt}t=−∞. We say that {yt} is (strictly) stationary if the joint distribution of the vectors (yt1 , . . . , ytk) and (yt1+s, . . . , ytk+s) are the same for any choice of the subscripts (t1, t2, . . . , tk, s). Thus, in particular, the marginal distributions are identical, so Eyt = μ and V yt = σ 2 are independent of t, and furthermore covariances Cov (yt, yt+s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2018
ISSN: 1935-7524
DOI: 10.1214/18-ejs1503