| Interface | Description |
|---|---|
| TimeSeriesProcessor<ALLDATA,SINGLEDATA,TIMESERIES extends TimeSeries<ALLDATA,SINGLEDATA,TIMESERIES>> |
A time series processor alters a type of
TimeSeries in place. |
| Class | Description |
|---|---|
| GaussianTimeSeriesProcessor |
Calculates a moving average over a specified window in the past such that
data[t_n] = sum^{m}_{i=1}{data[t_{n-i}}
This processor returns a value for each time in the underlying time series.
|
| IntervalSummationProcessor<ALLDATA,DATA,TS extends TimeSeries<ALLDATA,DATA,TS> & TimeSeriesArithmaticOperator<DATA,TS> & TimeSeriesCollectionAssignable<DATA,TS>> |
Given time step calculate each timestep such that
value[timeStep(x)] = sum from x-1 to x as n [ timeStep(n) ]
The exact meaning of "sum" for any given timestep data must be defined.
|
| LinearRegressionProcessor |
Using a
LinearRegression model, a time series is used as input to
calculate the coefficients of a linear regression such that value = b * time
+ c
This is the simplest kind of model that can be applied to a time series |
| MovingAverageProcessor |
Calculates a moving average over a specified window in the past such that
data[t_n] = sum^{m}_{i=1}{data[t_{n-i}}
This processor returns a value for each time in the underlying time series.
|
| WindowedLinearRegressionProcessor |
An implementation of an autoregressive model such that Xt =
b*X{t-offset-window,t-offset} + c
|