Resample a time series:

Resampling with spacing smaller than the minimum time increment will add time stamps:

Resampling with spacing larger than the minimum time increment:

Resample time series with dates:

Select business days:

Select weekends:

Select Wednesdays:

Resample an irregular data:

Resample with step of 2:

The resampled time series is now regularly sampled:

This time series contains the number of steps taken daily by a person during a period of five months:

Analyze the number of steps depending on the day of the week:

Select the values for each day of the week:

Compute the mean number of steps for each day of the week:

Visualize the mean steps per day:

Financial information is generated only for business days:

The automatically created time series is not regularly sampled:

Resample according to "BusinessDay" to create a uniformly sampled time series:

The paths are the same:

Consider some financial data:

The data is generated only for business days. There are no changes on the remaining days; hence, we can resample by day by keeping the value from the left:

The plot is flat over the weekends and holidays:

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

The data is not regularly sampled:

To analyze the rate of change, data needs to be resampled into a regularly sampled time series:

Plotting each observation disjointly shows the rate of change of the pressure, with larger spacing indicating faster changes:

Analyze the monthly temperatures in Champaign during 2014:

The raw data comes in one-day increments:

Resample by a month:

Basic descriptive statistics:

Compare to the original data:

The original time stamps may not be preserved after resampling:

Note that the time series is not regularly sampled:

Resample according to MinimumTimeIncrement:

New times: