Map a function f over data with a window of width two units of time:

Map a function f over data with a three-events window:

Compute a moving quantile for some data:

Use centered window of unit size:

Find a moving quartile envelope:

Find a yearly moving average of SP500 time series:

Find a yearly moving average of the time series, placing windows monthly:

Place windows monthly at the first day of each month and use reflected value padding:

Create a generic moving function for a vector:

Compute a moving average for a list of time-value pairs:

Compute a moving GeometricMean for a TimeSeries:

Compute a moving median for a TemporalData:

Compute a moving total for an EventSeries:

Compute a moving variance for multiple paths simultaneously:

Compute a moving correlation coefficient between two components of a bivariate time series:

Moving correlation coefficient over window of size 100:

Apply a generic function to values within moving windows:

Use a pure function instead:

Apply a generic function to values and time stamps within moving windows:

Use a pure function with named arguments:

Also provide the function with boundary times and resampled values of data at boundary times:

Define a function to compute both times and values for each window:

Use it to effectively associate window average with scaled time point within the window:

Define a function that drops missing values and their corresponding times:

Temperature readings in Champaign, IL:

Time series contains some missing values:

Delete data points with missing values:

Compute the mean temperature in January:

Find the number of days between consecutive Thanksgiving holidays:

Use numeric width of the moving window:

Use time quantities to specify extent of the moving window:

Specify window size using pairs {n,"unit"}:

Find daily median temperature every 4 hours:

Visualize the time series, showing seasonal temperature quartiles:

Specify window size by the number of events it contains:

Find moving average number of events per week:

Right-aligned windows use past values:

Right alignment is used by default:

Centered windows use both past and future values:

Left-aligned windows use future values:

Compare window alignments:

Because the time series is regular, values of moving averages for each alignment are equal:

The times are not:

A visual comparison:

Place windows automatically at every data point:

Place windows at every data point within an interval:

Place windows at every data point after a given instant:

Place windows uniformly:

Place windows uniformly within a given range:

Place windows at the given time stamps:

Find the highest stock value within the last two quarters for each date of interest:

With Automatic padding, only those windows that do not overhang the data time domain are used:

Automatic padding is used by default:

With padding set to None, some windows may overhang the data time domain:

Use Automatic time padding and constant value padding:

Temperature readings in Savoy, IL, around Valentine's Day at about one-minute resolution:

Compute the hourly moving average at resolution of 20 minutes:

Identify periods of high volatility in the S&P 500:

Five-year moving standard deviation:

Annual moving interquartile range:

Two-year moving range:

Compute a moving average of a regular time series, assigning more weight to recent values:

Compare to placing more weight on past values:

Use a centered window, placing the most weight on central values:

Compute a moving-time average of irregularly sampled continuous-time series:

Weights for the integral of a linear interpolation function over its domain:

Time average of linear interpolation of {{t₁,v₁},…,{t_n,v_n}} over the interval {t₁,t_n}:

Simulate a trajectory with heavy-tailed measurement noise:

The underlying signal and simulated path with noise:

Smooth the trajectory using a moving TrimmedMean:

Increasing the window size gives a smoother trajectory:

Smooth an object's trajectory with measurement noise in 3D space:

Compare the volatility of GARCH and ARMA processes:

The stationary mean and variance are equivalent for the processes:

For GARCH processes, the volatility exhibits characteristic spikes:

Minute‐resolution temperature readings in Savoy, IL, around Valentine's Day:

Specify a function to find the time and value of the highest temperature in the window:

Specify a function to find the time and value of the lowest temperature in the window:

Specifications for 1-day-long, left-aligned windows placed at the beginning of each day:

Build a sample of OrnsteinUhlenbeckProcess from a sample of WienerProcess:

Using , find and from and :

Compute a moving map over one event window, with a function that maps Wiener path time-value pair to Ornstein–Uhlenbeck path time-value pair:

Visualize simulated paths and compare to the process mean function: