Database Reference
In-Depth Information
8.3 Additional Methods
Additional time series methods include the following:
Autoregressive Moving Average with Exogenous inputs
(ARMAX) is used to analyze a time series that is dependent on another
time series. For example, retail demand for products can be modeled based
on the previous demand combined with a weather-related time series such
as temperature or rainfall.
Spectral analysis is commonly used for signal processing and other
engineering applications. Speech recognition software uses such techniques
to separate the signal for the spoken words from the overall signal that may
include some noise.
Generalized Autoregressive Conditionally Heteroscedastic
(GARCH) is a useful model for addressing time series with nonconstant
variance or volatility. GARCH is used for modeling stock market activity
and price fluctuations.
Kalman filtering is useful for analyzing real-time inputs about a system
that can exist in certain states. Typically, there is an underlying model of
how the various components of the system interact and affect each other. A
Kalman filter processes the various inputs, attempts to identify the errors
in the input, and predicts the current state. For example, a Kalman filter in
a vehicle navigation system can process various inputs, such as speed and
direction, and update the estimate of the current location.
Multivariate time series analysis examines multiple time series and
their effect on each other. Vector ARIMA (VARIMA) extends ARIMA by
considering a vector of several time series at a particular time, t. VARIMA
can be used in marketing analyses that examine the time series related to a
company's price and sales volume as well as related time series for the
competitors.
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