Using Wavelets and Splines to Forecast Non-Stationary Time Series

Discrimination of locally stationary time series using wavelets

Time series are sometimes generated by processes that change suddenly from one stationary regime to another, with no intervening periods of transition of any significant duration.Agood example of this is provided by seismic data, namely,waveforms of earthquakes and explosions. In order to classify an unknown event as either an earthquake or an explosion, statistical analysts might be helped by ...

Stationary 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...

Introduction 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...

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

Analysis of Non-Stationary Time Series using Wavelet Decomposition

Abstract: The increased computational speed and developments in the area of algorithms have created the possibility for efficiently identifying a well-fitting time series model for the given nonstationary-nonlinear time series and use it for prediction. In this paper a new method is used for analyzing a given nonstationary-nonlinear time series. Based on the Multiresolution Analysis (MRA) and n...

Non-stationary Queue Simulation Analysis Using Time Series

In this work, we extend the use of time series models to the output analysis of non-stationary discrete event simulations. In particular, we investigate and experimentally evaluate the applicability of ARIMA(p, d, q) models as potential meta-models for simulating queueing systems under critical traffic conditions. We exploit stationarity-inducing transformations, in order to efficiently estimat...