About Time Series analyses
A time series analysis is useful for exploring how sample measurements change over time. For example, a time series analysis can reveal how a sample evolves during a TGA-IR or GC-IR experiment.
Opening standard measurements as a time series analysis can help you identify trends and patterns in your data over time.
Time Series analyses can also be created from a set of repeated measurements taken during a workflow. For example, use a Repeat tile and a Delay tile to measure the same sample every minute or every hour. After running the workflow, open the measurements in a time series analysis to explore how the sample changed over the duration of the workflow.
Use tags to organize your measurements and to make them easier to convert to a time series analysis later. Workflows can use the Tag tile to automatically tag all measurements, and individual measurements can apply a tag during data collection using the Tag setting on the dashboard.
Key concepts and terms
-
Time Series: A time series analysis consists of a set of 4 or more measurements plotted along a time period.
Refer to "Create a Time Series analysis" for instructions on how to convert measurements to a time series analysis.
-
Profile: A profile is a representation of the series data and is used to show how the data changes over time. Each profile shows a different aspect of the measurement data, such as how a peak height or peak area changes.
Refer to "Profile types" for a description of each profile.
Refer to "Create and use profiles" for instructions on how to apply, save, and edit profiles.
-
History: In a time series analysis, the history includes all processing and display conversion operations. You can use the history to see what processing operations have been applied to the data and to remove specific operations.
Refer to "Process time series data" for instructions on applying and removing processing operations
Comparison of time series searches
-
Identify Time Series Peaks: This search finds peaks in the profile and then does a correlation search on the spectrum at each peak, providing a list of best matches for every peak it identifies. This is useful for telling you what materials appear at specific times in the measurement. Use this search when your profile has distinct peaks, such as during a GC-IR measurement.
-
Identify Components: This search is like running a multi-component search across the entire time series measurement. The search returns a composite spectrum and the profile of each of the library matches, showing you the gradually changing response of each of the major components. This search is useful when your profile has more gradual changes, such as in a TGA-IR measurement.
The analysis duration depends on the number of reference libraries selected, the size of each library, and the number of response peaks in the series Gram-Schmidt profile.