README
¶
timeseries
Package timeseries provides utilities to manipulate and analyze timeseries data. For compatability with Gonum, a Timeseries is simply a pair of float64 slices, representing the X and the Y axis. You can manipulate them as you wish, but ensure two things:
-
Many of the methods in this library assume that the data is sorted. If you do not insert in sorted order, ensure that you call Sort()
-
Ensure that Timeseries.Xs and Timeseries.Ys is always of equal length if you manipulate them without the accessors provided. Violating this constraint will result in pancis.
At the time of this writing(May, 2018), please do not assume API stability.
License
timeseries is licensed under MIT. See LICENSE
Documentation
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Overview ¶
Package timeseries provides utilities to manipulate and analyze timeseries data. For compatability with Gonum, a Timeseries is simply a pair of float64 slices, representing the X and the Y axis. You can manipulate them as you wish, but ensure two things:
- Many of the methods in this library assume that the data is sorted. If you do not insert in sorted order, ensure that you call Sort()
- Ensure that Timeseries.Xs and Timeseries.Ys is always of equal length if you manipulate them without the accessors provided
Index ¶
- type Timeseries
- func (t Timeseries) After(x float64) Timeseries
- func (t *Timeseries) Append(x float64, y float64)
- func (t Timeseries) At(i int) (x, y float64)
- func (t Timeseries) Before(x float64) Timeseries
- func (t Timeseries) Between(x1, x2 float64) Timeseries
- func (t Timeseries) Difference() (ret Timeseries)
- func (t Timeseries) Equal(other Timeseries) bool
- func (t Timeseries) First() (x, y float64)
- func (t Timeseries) Last() (x, y float64)
- func (t Timeseries) Len() int
- func (t Timeseries) Less(i, j int) bool
- func (t Timeseries) LinearRegression() (alpha, beta, rmse float64)
- func (t Timeseries) MovingAverage(window int) (ret Timeseries)
- func (t Timeseries) Slice(start, end int) Timeseries
- func (t Timeseries) Sort()
- func (t Timeseries) Swap(i, j int)
- func (t Timeseries) WeightedLinearRegression(weights []float64) (alpha, beta, rmse float64)
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type Timeseries ¶
func (Timeseries) After ¶
func (t Timeseries) After(x float64) Timeseries
After - Return a shallow copy of the items in the time series having Xs >= x The series must be sorted.
func (*Timeseries) Append ¶
func (t *Timeseries) Append(x float64, y float64)
Append - Append value @ time to the timeseries Note that you might need a sort if you're inserting points out-of-order
func (Timeseries) At ¶
func (t Timeseries) At(i int) (x, y float64)
At - return the x, y pair at index i If i does not represent a valid index, At panics
func (Timeseries) Before ¶
func (t Timeseries) Before(x float64) Timeseries
Before - Return a shallow copy of the items in the time series having Xs < x. The series must be sorted.
func (Timeseries) Between ¶
func (t Timeseries) Between(x1, x2 float64) Timeseries
Between - Return a shallow copy of the items in the time series between [x1, x2)
func (Timeseries) Difference ¶
func (t Timeseries) Difference() (ret Timeseries)
Difference the timeseries N, returning a new series of length len(N)-1
func (Timeseries) Equal ¶
func (t Timeseries) Equal(other Timeseries) bool
Equal - Return true if t and other represent the same time series
func (Timeseries) First ¶
func (t Timeseries) First() (x, y float64)
First - Return the first x, y value of the timeseries. If the timeseries contains no items, First() panics.
func (Timeseries) Last ¶
func (t Timeseries) Last() (x, y float64)
Last - Return the last x, y value of the timeseries. If the timeseries contains no items, Last() panics.
func (Timeseries) Len ¶
func (t Timeseries) Len() int
Len - return the lenght of the timeseries. If len(Timeseries.Xs) != len(Timeseries.Ys), Len() panics
func (Timeseries) Less ¶
func (t Timeseries) Less(i, j int) bool
func (Timeseries) LinearRegression ¶
func (t Timeseries) LinearRegression() (alpha, beta, rmse float64)
SimpleLinearRegression performs a simple linear regression of the series computing the best fit line
y = alpha + beta*x
such that rmse is minimized
func (Timeseries) MovingAverage ¶
func (t Timeseries) MovingAverage(window int) (ret Timeseries)
MovingAverage returns a time series representing the window-sized moving average over t
func (Timeseries) Slice ¶
func (t Timeseries) Slice(start, end int) Timeseries
Slice slices the Timeseries equivalently to t[start:end]
func (Timeseries) Sort ¶
func (t Timeseries) Sort()
func (Timeseries) Swap ¶
func (t Timeseries) Swap(i, j int)
func (Timeseries) WeightedLinearRegression ¶
func (t Timeseries) WeightedLinearRegression(weights []float64) (alpha, beta, rmse float64)
WeightedLinearRegression performs a weighted linear regression of the series computing the best fit line
y = alpha + beta*x
such that rmse is minimized
Source Files