Time series
occur frequently when looking at industrial data
There are many methods use to model and forecast Time Series
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Definition of Time Series:
An
ordered sequence of values of a variable at equally spaced time intervals.
Applications: The usage of time series models is twofold:
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Obtain an understanding of the underlying forces and structure that produced
the observed data
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Fit a model and proceed to forecasting, monitoring or even feedback and
feed forward control.
Time Series Analysis is used for many applications such as:
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Economic Forecasting
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Sales Forecasting
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Budgetary Analysis
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Stock Market Analysis
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Yield Projections
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Process and Quality Control
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Inventory Studies
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Workload Projections
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Utility Studies
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Census Analysis
and many, many more...
Techniques: The fitting of time series models can be an ambitious
undertaking. There are many methods of model fitting including the following:
The user's application and preference will decide the selection of the
appropriate technique. It is beyond the realm and intention of this handbook
to cover all these methods. The overview presented here will start by looking
at some basic smoothing techniques:
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Averaging Methods
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Exponential Smoothing Techniques.
Later in this section we will discuss the Box-Jenkins modeling methods
and Multivariate Time Series. |
