Visualize a time series:

Use TimeSeriesWindow to extract a portion of a time series:

Use TimeSeriesInsert to replace a missing value:

Use TimeSeriesRescale to rescale a time series to run from 0 to 20:

Use TimeSeriesShift to shift the series ahead by 2:

Square the values in a time series:

Use TimeSeriesMap to find the sums of the components of a vector-valued time series:

Find the Mean of a time series:

The mean depends only on the values:

Compute a weighted mean:

Compute a MovingAverage for a time series:

Use MovingMap to compute a moving maximum:

Use TimeSeriesAggregate to compute the weekly totals for a time series:

Fit a parametric model to a time series using TimeSeriesModelFit:

Use TimeSeriesForecast to forecast the next 10 values in the time series:

Plot the time series along with the forecast:

Use TimeSeriesThread to compute the differences between two time series:

Give a list of values with Automatic time stamps:

Create a time series starting at :

Use dates for starting times:

Dates can be given as any valid input to AbsoluteTime:

Use equally spaced times from 10 to 50:

Give a range of dates to use:

Specify an Automatic endpoint:

Create a series with times 1 to 20 in steps of 2:

Use an Automatic endpoint and fixed step:

Extract the computed last date:

Use an Automatic start point and given frequency:

Extract the computed first time:

Explicitly specify the times to use:

Give an explicit list of dates:

Give the time-value pairs:

Create a time series from date-value pairs:

Create a time series from data involving quantities:

Obtain a list of available properties:

Values used for the time series:

Times:

Time-value pairs:

Plot the time series:

Represent the time series as a function:

The path function:

Extract components of vector-valued collection:

The first component:

Obtain the second component:

Plot the paths components:

Resample data over a given set of times:

Upsample the original path in steps of 0.25:

The new data is sampled from the path function:

Time series involving quantities:

The values are given as QuantityArray:

Extract quantity unit information:

Extract quantity magnitudes:

Numerical, listable functions automatically thread over values of time series:

Compare to the result of TimeSeriesMap:

Combining several time series with identical time stamps threads over values:

Time series are resampled at the union of time stamps in the intersection of their supports:

The intersection of supports is , and the union of the time stamps within is :

Compare the result with adding the resampled time series:

Create a new time series of quantity magnitudes from existing time series involving quantities:

Create a new time series of quantity units:

Specify time stamps as dates in a specific calendar using CalendarType:

By default, the "Gregorian" calendar is being used:

Specify input dates in "Gregorian" calendar but display in "Jewish" calendar:

Use DateList to define functions for interpreting ambiguous date strings:

Use DateObject to define functions for interpreting ambiguous date strings:

Specify the TimeZone of the inputs:

Use HolidayCalendar to visualize business days in a given country:

Include additional meta-information as a list of rules:

The properties now include the meta-information "Stock":

The added meta-information can be used like any other property:

Use MetaInformation to specify PlotLegends:

See the available MetaInformation:

Access specific information directly:

Visualize the data:

Use MetaInformation to name the components in a vector-valued TimeSeries:

Extract second component:

Extract first and third components using either their name or number:

By default, values with head Missing are interpreted as missing:

Use MissingDataMethod to replace missing values with a constant:

Use interpolation to replace the missing values:

The default method is interpolation of order 1:

By default, values at intermediate times are computed using first-order interpolation:

Use ResamplingMethod to assign a constant value at intermediate times:

Use zero-order interpolation:

Explicitly assume that temporal data is regularly spaced:

Specify the time zone of TimeSeries:

The time stamps were created in $TimeZone, but the dates are displayed in the time zone specified by the option:

Specify the dimensionality of the values:

Use SunPosition to generate the Sun's position in Chicago for a range of dates:

Plot the variation of the azimuth and the altitude for this period:

Use MoonPosition to generate the Moon's position for a range of dates:

Verify that the Moon's orbit is tilted with respect to the Earth's equator:

Use CountryData to generate the GDP for UK and Germany:

Compare the GDP of these two countries:

Forecast stock prices:

Fit an ARIMA process:

Forecast to the next half a year:

Average temperature on the first day of a month in Chicago, IL:

Fit a SARIMA process:

Forecast the average temperatures on the first day of a month for the next three years:

Use NuclearReactorData to visualize energy production for the Chernobyl reactors:

Use AirPressureData to examine pressure reading drops due to Hurricane Sandy at Long Island MacArthur Airport:

Use WindSpeedData to compare the wind speeds at John F. Kennedy Airport during summer and winter:

Capture a 10-second time series at 0.05-second intervals:

Plot the time series along with a moving average:

The following time series is generated by reading illuminance data from a TinkerForge Weather Station every 0.1 second for 5 seconds while continuously changing the device orientation:

Plot the illuminance values:

Min, Mean, and Max for the data:

Generate illuminance data by alternately switching the ambient light source on and off:

A plot of the time series reveals the approximate periodic nature of the data:

Verify the periodicity using Fourier:

Use MeanFilter to filter a time series:

The following data represents annual sales for a small software company for 11 years:

Use LinearModelFit to fit a linear model to this data:

Plot the original data along with the values obtained using the linear model:

Apply exponential smoothing with weight 0.45 to the data:

Plot the original data along with the values obtained using exponential smoothing:

Fit a parametric model to retail sales data for the US between 1992 and 2015:

Construct a time series model for the data using TimeSeriesModelFit:

Use the model to forecast the next three months: