Data considerations for Time Series Plot

For the graph to represent your data most effectively, consider the following guidelines.

Record data in chronological order

Time series data are collected at regular intervals and are recorded in time order. You should record the data in the worksheet in the same order that you collect it. If the data are not in chronological order, you cannot use a time series plot to assess time-related patterns in the data.

If you collect data for multiple groups at each interval, it is best to enter the data for each group in a separate column (also called multiple Y variables). For example, each column in this worksheet contains the number of parts that were created on a specific machine each day.

C1 C2
Machine 1 Machine 2
5196 4367
4563 4100
4659 4451
... ...

Collect data at regular time intervals

Time Series Plot and other time series analyses assume that data are collected at regular intervals, such as once a day, or once a month. If you collect data at irregular intervals, then a time series plot may be misleading.

If you collect data at irregular intervals, consider using Scatterplot. For example, if you collect data on days 1, 2, 4, 8, and 16, then you can use a scatterplot to plot the measurement data on the y-axis and the number of days (1, 2, 4, 8, and 16) on the x-axis.

Collect data at appropriate time intervals

Choose the time interval based on the patterns that you want to detect. For example, to look for month-to-month patterns in a process, collect data at the same time each month. Collecting data less frequently will not allow you detect monthly patterns. Collecting data more frequently could add unnecessary noise to the data and obscure monthly patterns.

If you are looking only for general trends or shifts in the data over time, and not for patterns associated with a specific time interval, the length of the interval is less important.

Collect enough data to assess trends or patterns
Collect enough data so that you can fully assess trends or patterns in the data. For example, you need enough data to be sure that any pattern you observe is a long-term pattern and not just a short-term anomaly.
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