These are a versatile family of models for modeling and forecasting time series data.
Seasonal ARIMA models have a special form for efficiently modeling many
kinds of seasonal time series and are heavily used in seasonal adjustment.
ARIMA is an acronym for AutoRegressive Integrated Moving Average.
Time series measuring consecutive changes over a unit of time,
such as monthly sales or monthly cash flows, or monthly changes in any stock series.
This is the residual time series that results from the removal of estimated seasonal
and other systematic calendar-related components of an observed time series,
along with the removal of an estimated trend-cycle component.
Moving holiday effects
These are systematic changes in the values of a time series that are associated
with the timing of moving holidays, i.e. holidays whose dates vary from year to year,
such as Easter, Passover, Ramadan, Chinese New Year and U.S. Labor Day.
Estimates of one or a combination of such effects define the moving holiday component
of time series.
RegARIMA models (Also regression+ARIMA models.)
In the seasonal adjustment context, a hybrid model in which some features of the
time series, such as moving holiday, trading day and outlier effects, are modeled with
linear regression variables while the remaining features (those of the regression residuals,
including trend, cycle and seasonal components) are modeled with a seasonal ARIMA model.
The estimation of the seasonal component and, when applicable, also trading day and
moving holiday effects, followed by their removal from the time series.
The goal is usually to produce series whose movements are easier to analyze over
consecutive time intervals and to compare to the movements of other series in order
to detect co-movements.
A time series whose values quantify (usually in percents or in the units of data measurement,
e.g. dollars) variations in the level of the observed series that recur with the same direction
and a similar magnitude at time intervals of length one year. (Length is measured in the calendar
units of the observed series--usually quarters or months, sometimes semesters, weeks,
or other units.)
SEATS (Signal Extraction in ARIMA Time Series)
This approach is used within TRAMO-SEATS and also within the X-13A-S seasonal adjustment packages.
It can simultaneously estimate the different components of a time series.
For the unit of time of the series, months for example, these are time series like
end-of-month inventories that arise as the cumulative sum of inflows and outflows
(i.e. monthly net flows) starting from some initial value in the past.
A sequence of measurements of an economic (or other) variable made at approximately
equally spaced times. It is important that the definition of the variable and
the method used to measure it be consistent over time.
Trading day effects
As practical concerns, these are systematic effects in monthly times series
related to changes in the day-of-week composition of each month and, in some cases,
also to changes in the length of February. For flow series (monthly accumulations of
daily activity e.g. monthly sales), the increases or decreases from average day-of-week
activity associated with the days that occur five times in the month in a given year
are important. (If they are days of high sales volumes, the monthly value will be inflated,
etc.) For flow series, the length of February can have an impact.
(More days than average should produce more sales than average for February.)
For stock series, such as end-of-month inventories, the extent to which inventories
tend to rise or fall on the day of measurement (e.g. the last day of the month)
can have an impact that is different from year to year. Attempts to measure analogous
effects in quarterly series are seldom successful. A series of estimated trading day effects
defines a trading day component for the time series.
TRAMO (Time Series Regression with ARIMA noise)
This approach is used within TRAMO-SEATS seasonal adjustment sofware. It is an approach to estimate and prior correct time series before seasonal adjustment.
Seasonal adjustment software developed by the Bank of Spain. It uses models to estimate the different time series components.
This is an estimate of the local level of the time series that is expected to
include the effects of moderately short- and well as long-term movements associated
with the "business cycle". It is often obtained by applying a
customized smoothing procedure (a data-dependent "trend filter")
to the seasonally adjusted series to suppress its oscillatory movements over
short time intervals, i.e. its higher frequency movements.
Seasonal adjustment software originally developed by United States Census Bureau.
It is based on an iterative application of linear filters.
A Seasonal adjustment software developed by Statistics Canada.
It incorporates ARIMA modelling to improve estimation of the different time series components.
A Seasonal adjustment software developed by the United States Census Bureau.
It incorporates regression techniques and also ARIMA modelling to improve estimation
of the different time series components.
Seasonal adjustment software under development at the U. S. Census Bureau
in collaboration with the Bank of Spain that integrates an enhanced version of
X-12-ARIMA with an enhanced version of SEATS to provide both X-11 method
seasonal adjustments and ARIMA model-based seasonal adjustments and diagnostic.