| Constructor and Description |
|---|
Filtration(UnivariateTimeSeries<Double,? extends UnivariateTimeSeries.Entry<Double>> Bt)
Construct a
Filtration from a Brownian path. |
| Modifier and Type | Class and Description |
|---|---|
class |
DateTimeTimeSeries
This is a time series has its
double values indexed by
LocalDateTime. |
class |
GenericTimeTimeSeries<T extends Comparable<? super T>>
This is a univariate time series indexed by some notion of time.
|
| Modifier and Type | Method and Description |
|---|---|
static Matrix |
UnivariateTimeSeriesUtils.toMatrix(UnivariateTimeSeries<?,?> ts)
Cast a time series into a column matrix, discarding the timestamps.
|
static Vector |
UnivariateTimeSeriesUtils.toVector(UnivariateTimeSeries<?,?> ts)
Cast a time series into a vector, discarding the timestamps.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Realization
This is a univariate time series indexed real numbers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
OneDimensionTimeSeries<T extends Comparable<? super T>>
This class constructs a univariate realization from a multivariate realization by taking one of its dimension (coordinate).
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
IntTimeTimeSeries
This is a univariate time series indexed by integers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DifferencedIntTimeTimeSeries
Differencing of a time series \(x_{t}\) in discrete time t is the
transformation of the series to a new time series \((1-B)x_{t}\) where the new values
are the differences between consecutive values of \(x_{t}\).
|
class |
SimpleTimeSeries
This simple univariate time series simply wraps a
double[] to form a time series. |
| Modifier and Type | Class and Description |
|---|---|
class |
AdditiveModel
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
\[
Y[t] = T[t] + S[t] + e[t]
\]
|
class |
MultiplicativeModel
The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.
\[
Y[t] = T[t] \cdot S[t] \cdot e[t]
\]
|
| Constructor and Description |
|---|
Kagi(UnivariateTimeSeries<T,? extends UnivariateTimeSeries.Entry<T>> ts) |
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