An Overview One of the most important and challenging problems in time series modeling is to correctly forecast a particular time series by observing its distribution. This behavior of time series arises urgent need of developing generalized time series models for a particular statistical distribution.

An Overview Parametric inference is the process by which information from sample data is used to draw conclusions about the population properties from which the sample was selected. The distribution of the population from which sample is drawn is assumed to be known and can be modeled by a probability distribution that have a fixed set of parameters.

An Overview Statistical computing can be broadly described as computational, graphical, and numerical approaches to solving statistical problems. It deals with numerical methods and algorithms, such as optimization and random number generation.

Introduction Components of Time Series Long-Term Trend Short-Term Fluctuations Random/Irreguar Movements Introduction A set of ordered data points taken at successive intervals in time is known as time series.

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