Results 261 to 270 of about 39,145,954 (311)
SSRN Electronic Journal, 2006 Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the ...
Francis X. Diebold +2 more
openaire +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
A Framework for Time-Series Analysis
2010The popularity of time-series databases in many applications has created an increasing demand for performing data-mining tasks (classification, clustering, outlier detection, etc.) on time-series data. Currently, however, no single system or library exists that specializes on providing efficient implementations of data-mining techniques for time-series
Vladimir Kurbalija +3 more
openaire +2 more sources
nonlinear time series analysis [PDF]
, 2008 Since the early 1980s, there has been a growing interest in stochastic nonlinear dynamical systems of the form, where is a zero mean, covariance stationary process, is the conditional volatility, and is an independent and identically distributed noise process.
openaire +2 more sources
Permutations and time series analysis
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2009The main aim of this paper is to show how the use of permutations can be useful in the study of time series analysis. In particular, we introduce a test for checking the independence of a time series which is based on the number of admissible permutations on it.
Cánovas, Jose S., Guillamón, Antonio
openaire +3 more sources
Foundations of Time Series Analysis
2021For almost a century, classical statistical methods including exponential smoothing and autoregression integrated moving averages (ARIMA) have been predominant in the analysis of time series (TS) and in the pursuit of forecasting future events from historical data.
Jonas, Ort +5 more
openaire +2 more sources
ANALYSIS OF TIME SERIES WITH WAVELETS
International Journal of Wavelets, Multiresolution and Information Processing, 2007A financial time series analysis method based on the theory of wavelets is proposed. It is based on the transformation of data of the series in the corresponding wavelet coefficients and in the analysis of the latter, which represent the local characteristics of the series better. In particular, an algorithm for short term previsions is defined.
openaire +3 more sources
2018
Analysis of epidemic time series is a large endeavor because of the richness of dynamical patterns and plentitude of historical data (Rohani and King 2010). A wide range of tools are used, some of which are borrowed from mainstream statistics other of which are “custom made.” The classic “mainstream” methods belong to two categories: the so-called time-
openaire +1 more source
Analysis of epidemic time series is a large endeavor because of the richness of dynamical patterns and plentitude of historical data (Rohani and King 2010). A wide range of tools are used, some of which are borrowed from mainstream statistics other of which are “custom made.” The classic “mainstream” methods belong to two categories: the so-called time-
openaire +1 more source
Wavelets in time-series analysis
Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1999Abstract Reviewing the role of wavelets in statistical time-series analysis (TSA) appears to be quite an impossible task. For one thing, wavelets have become so popular that such a review could never be exhaustive. Another, more pertinent, reason is that there is no such thing as one statistical time-series analysis, as the very many ...
Nason, GP, von Sachs, R
openaire +2 more sources
Time series reconstruction analysis
2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016The dimensionality of time series data is usually very large, so it must often be reduced before applying certain data mining tasks upon it. Dimensionality reduction is achieved by creating appropriate time series representation that is actually new time series of lower dimensionality obtained from the original one by preserving only the important ...
Kurbalija, Vladimir, Bratić, Brankica
openaire +2 more sources
Time series analysis of injuries
Statistics in Medicine, 1989AbstractWe used time series models in the exploratory and confirmatory analysis of selected fatal injuries in the United States from 1972 to 1983. We built autoregressive integrated moving average (ARIMA) models for monthly, weekly, and daily series of deaths and used these models to generate hypotheses.
B, Martinez-Schnell, A, Zaidi
openaire +2 more sources