# TimeSeriesMap

TimeSeriesMap[f,tseries]

applies f to the values in tseries.

# Details • TimeSeriesMap is used to apply functions to the values in a time series.
• TimeSeriesMap can be used for regularly and irregularly spaced time series.
• • The input tseries can be a list of values {x1,x2,}, a list of time-value pairs {{t1,x1},{t2,x2},}, a TimeSeries, an EventSeries, or TemporalData.
• TimeSeriesMap threads pathwise for tseries with multiple paths.

# Examples

open allclose all

## Basic Examples(2)

Map a function f over the values in a time series:

Find totals for each value in a multivariate time series:

## Scope(9)

### Basic Uses(3)

Map a function over a time series:

Center a time series:

Use Standardize:

Use TimeSeriesMap to find the norm of each value of a vector-valued time series:

### Data Types(6)

Map a function over a vector:

Set extreme values to Missing for a list of time-value pairs:

Double the values of a TimeSeries:

Add 50 to the values of TemporalData:

Represent the events in an EventSeries by alternating colored disks:

Add a Quantity to a time series with quantities:

## Applications(7)

Visualize current maximum prices of stock over a given investment horizon:

Use TimeSeriesMap to calculate determinants of covariance matrices for an ARProcess:

Replace Missing[] differently for each component of vector-valued TimeSeries using:

Visualize the evolution of rail lines in Japan, starting in 1980:

Check the units in which the length is given:

Convert the length to kilometers:

Study unemployment in France:

Unemployement rates:

Convert rates to fractions:

The unemployment is the corresponding fraction of the labor force:

Each value component corresponds to one gender:

Use TimeSeriesMap to find the total unemployement:

Time series of wind speeds in Champaign, IL, in May 2014:

Specify how to handle missing values:

Use TimeSeriesMap to build the time series for the power output of a 1.5 MW wind turbine:

Visualize the daily moving average of the energy output:

Simulate GeometricBrownianMotionProcess by transforming a WienerProcess where :

Apply the transformation to a random sample to obtain the geometric Brownian motion:

Compare to the corresponding GeometricBrownianMotionProcess:

Both simulations should have the same statistical properties, including for slices:

## Properties & Relations(1)

Some operations can be obtained using listability:

Obtain the same result using listability:

## Neat Examples(2)

Visualize market share of web browsers:

The time series contains the percentage of market share for each month:

List of all browsers:

Extract top seven dominant browsers for each month:

Create a phase plot of sunrise and sunset times through a year:

The values are event times up to a minute:

The data does not account for daylight saving time:

Convert the events to a time in the 24-hour day: