Information AboutTime Series |
| CATEGORIES ABOUT TIME SERIES | |
| time series analysis | |
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In based on its past performance. Models for time series data can have many forms. Two broad classes of practical importance are the ''moving average'' (MA) models, and the ''autoregressive'' (AR) models. These two classes depend linearly on previous data points and are treated in more detail in the article on Autoregressive Moving Average Model s (ARMA). Non-linear dependence on previous data points is of interest because of the possibility of producing a Chaotic time series. A number of different notations are in use for time-series analysis : is a common notation which specifies a time series ''X'' which is indexed by the natural numbers. Tools for investigating time-series data include:
INDUSTRY USAGE Any associative array of times and numbers can be viewed as a time series. The times may not necessarily be of a regular interval length. For example, the historical fluctuations in the price of a NYMEX Gold Contract can be said to be the time series for NYMEX Gold. Analysts throughout the economy will use the tools outlined here to aid in the management of their corresponding businesses. Energy traders, for example, will often attempt to forecast power consumption based upon both weather normals and short term weather forecasts. SEE ALSO
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