Contents
How to make forecasts of equidistant time series?
There are several methods to make forecasts of equidistant time series (e.g. Holt-Winters, ARIMA.). However I am currently working on the following irregular spaced data set, which has a varying amount of data points per year and no regular time intervals between those points:
Can a sensor be converted to an evenly spaced time series?
These event-triggered sensors give rise to unevenly-spaced time series. Many analysts will immediately convert unevenly-spaced data to evenly-spaced time series to be compatible with existing sensor data analytics tools, but we have found that the conversion is usually unnecessary, and sometimes even causes problems.
How are evenly spaced time series stored in Excel?
Since evenly-spaced time series are usually stored without timestamps (in an array, along with the start time and time interval), it’s easy to use the wrong time units — conversions are notoriously error-prone — or mix up when the data was recorded.
When to sample at the smallest time resolution?
If you have data from several sources with different time resolution, the usual approach is to sample at the smallest time resolution. You can end up with a time series that is sampled every second from a sensor that samples every hour.
Why do we use an irregular time series?
Bottom line, oversampling is a useful method for better resource allocation. We can view irregular time series as some sort of oversampling, provided there are no missing values and irregular intervals in the chart are consistent with intervals in the time series.
Can you use R forecast package with missing values?
The thing is: all the nice magic of the forecast package, such as eta (), auto.arima () etc, seem to expect plain ts objects, i.e. equispaced time series not containing any missing data. I think real world applications for equispaced-only time series are definitely existent, but – to my opinion – v e r y limited.
What do you do with irregular dates in a chart?
In a survey, you weight the subgroup down to its right proportion, and that’s also what you do in a chart, when irregular date intervals are displayed proportionally. Using a line to connect values along unequal intervals of time or to connect intervals that are not adjacent in time is misleading.