trait Operators extends AnyRef
Discrete wavelet transform (DWT).
- Alphabetic
- By Inheritance
- Operators
- AnyRef
- Any
- by any2stringadd
- by StringFormat
- by Ensuring
- by ArrowAssoc
- Hide All
- Show All
- Public
- All
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
- def +(other: String): String
- def ->[B](y: B): (Operators, B)
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
def
dwt(t: Array[Double], filter: String): Unit
Discrete wavelet transform.
Discrete wavelet transform.
- t
the time series array. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.
- filter
wavelet filter.
- def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators
- def ensuring(cond: (Operators) ⇒ Boolean): Operators
- def ensuring(cond: Boolean, msg: ⇒ Any): Operators
- def ensuring(cond: Boolean): Operators
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
- def formatted(fmtstr: String): String
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
def
idwt(wt: Array[Double], filter: String): Unit
Inverse discrete wavelet transform.
Inverse discrete wavelet transform.
- wt
the wavelet coefficients. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.
- filter
wavelet filter.
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
def
wavelet(filter: String): Wavelet
Returns the wavelet filter.
Returns the wavelet filter. The filter name is derived from one of four classes of wavelet transform filters: Daubechies, Least Asymetric, Best Localized and Coiflet. The prefixes for filters of these classes are d, la, bl and c, respectively. Following the prefix, the filter name consists of an integer indicating length. Supported lengths are as follows:
Daubechies 2,4,6,8,10,12,14,16,18,20.
Least Asymetric 8,10,12,14,16,18,20.
Best Localized 14,18,20.
Coiflet 6,12,18,24,30.
Additionally "haar" is supported for Haar wavelet. Although Haar wavelet is a special case of the Daubechies wavelet transform filter of length 2, the implementation of "haar" is different from "d2".
Besides, "d4", the simplest and most localized wavelet, uses a different centering method from other Daubechies wavelet.
- filter
filter name
-
def
wsdenoise(t: Array[Double], filter: String, soft: Boolean = false): Unit
The wavelet shrinkage is a signal denoising technique based on the idea of thresholding the wavelet coefficients.
The wavelet shrinkage is a signal denoising technique based on the idea of thresholding the wavelet coefficients. Wavelet coefficients having small absolute value are considered to encode mostly noise and very fine details of the signal. In contrast, the important information is encoded by the coefficients having large absolute value. Removing the small absolute value coefficients and then reconstructing the signal should produce signal with lesser amount of noise. The wavelet shrinkage approach can be summarized as follows:
- Apply the wavelet transform to the signal.
- Estimate a threshold value.
- The so-called hard thresholding method zeros the coefficients that are smaller than the threshold and leaves the other ones unchanged. In contrast, the soft thresholding scales the remaining coefficients in order to form a continuous distribution of the coefficients centered on zero.
- Reconstruct the signal (apply the inverse wavelet transform).
The biggest challenge in the wavelet shrinkage approach is finding an appropriate threshold value. In this method, we use the universal threshold T = σ sqrt(2*log(N)), where N is the length of time series and σ is the estimate of standard deviation of the noise by the so-called scaled median absolute deviation (MAD) computed from the high-pass wavelet coefficients of the first level of the transform.
- t
the time series array. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.
- filter
the wavelet filter to transform the time series.
- soft
true if apply soft thresholding.
- def →[B](y: B): (Operators, B)
High level Smile operators in Scala.