# 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.

## time series

any statistical information recorded over successive time periods. See TIME-SERIES ANALYSIS.
References in periodicals archive ?
The Box-Jenkins method (ARIMA) is one of the most widely used time series forecasting methods in practice [4].
Key Words: Production Forecasting, Exponential Smoothing, Time Series Data, Oil Plants.
This study on time series of prices for agricultural products was performed based on the commodities identified bellow as SUG (sugar), COT (cotton) COR (corn), COF (coffee), and SOY (soy).
2013) and the Quasi-Observation Combination Analysis based loading model (QLM) are widely used to estimate surface displacements and correct nonlinear variations in GPS weekly time series (Dong et al.
This technique uses a distributed storage technique on the converted time series data that breaks the data files into segments that contain a portion of the full time series dataset.
Statistical Analysis--Multiple time series were modeled as a function of a constant, a linear combination of 'M' common trends, explanatory variables, and noise.
SSS model is based on the same idea that change in time series between t and t + m will be the same to the change time series had in time period between t - p and t - p + m .
The one-screen option is a JavaScript application that uses a single screen to guide a user through the available time series data.
We propose a more robust technique to estimate the effective sample size for the case of an autoregressive process of order 1 (AR1), a suitable hypothesis for many time series in meteorology and climate sciences.
The main objective of this activity is to develop general analytical methods for the exploitation of the information contained in Satellite Image Time Series (SITS).
Fuzzy time series is based on many antecedents and single consequent.

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