time series

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Time Series

A comparison of a variable to itself over time. One of the most common time series, especially in technical analysis, is a comparison of prices over time. For example, one may compile a time series of a security over the course of a week or a month or a year, and then use it in the determination of future price movements.

time series

A set of variables with values related to the respective times the variables are measured. Thus, a weekly record of a stock's price throughout a period of years is a time series. Time series are often used to project future values by observing how the value of a variable has changed in the past.

Wall Street Words: An A to Z Guide to Investment Terms for Today's Investor by David L. Scott. Copyright © 2003 by Houghton Mifflin Company. Published by Houghton Mifflin Company. All rights reserved. All rights reserved.

time series

any statistical information recorded over successive time periods. See TIME-SERIES ANALYSIS.

Collins Dictionary of Economics, 4th ed. © C. Pass, B. Lowes, L. Davies 2005

References in periodicals archive ?

Bialonski, "Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains," Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol.

Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

Furthermore, the successful use of fuzzy-based similarity measure in pattern recognition [17], in retrieval systems [12], and in recommendation systems [18] leads us to study its ability to complete missing values in uncorrelated multivariate time series. Wang et al.

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

As it was previously stated, MSSA is an extension of the SSA to the case of multivariate time series. The majority of the MSSA algorithm steps are similar to the steps of univariate approach.

Deriving common seasonal signals in GPS position time series by using multichannel singular spectrum analysis

In Section 3, we detail the Cycle DBN model for multivariate time series. Section 4 evaluates the performance of our Cycle DBN on two real data sets.

A Cycle Deep Belief Network Model for Multivariate Time Series Classification

* We develop efficient algorithms which perform lossy compression on both univariate and multivariate time series data.

Efficient compression algorithm with limited resource for continuous surveillance

As we were interested in the potentially bidirectional effects of coverage and attitudes over time, we used multivariate time series analysis (VAR), which enabled us to model the dynamics between more than one time series while allowing for interdependence between the series.

Who's the Boss? Setting the Agenda in a Fragmented Media Environment

Estimating common trends in multivariate time series using dynamic factor analysis.

Modeling common trends of zooplankton, atherinopsid, and barred splitfin time series in a shallow tropical lake

Reimer, "Psychosocial predictors of metabolic instability in brittle diabetes--a multivariate time series analysis," Psychotherapie Psychosomatik Medizinische Psychologie, vol.

New methods in exploring old topics: case studying brittle diabetes in the family context

Chapter 8 addresses modeling multivariate time series, and nonstationary time series using various approaches including cointegration, autoregressive conditional heteroscedasticity (ARCH), and generalized ARCH (GARCH).

Developing Econometrics

Multivariate time series analysis; with R and financial applications.

Multivariate time series analysis; with R and financial applications

The process of line icing can be regarded as the icing load at time k, which is decided by icing load and micrometeorology factors at time k - 1, that is, a multivariate time series process.

Multivariable time series prediction for the icing process on overhead power transmission line

In practice, there are several emergent domains that require dealing with short multivariate time series. As a consequence, the prediction of such time series arises in many situations.

A new method for short multivariate fuzzy time series based on genetic algorithm and fuzzy clustering