What is a time series plot and how is it used in Six Sigma?

A time series plot is a graph showing a set of observations taken at different points in time and charted in a time series. These observations are usually successive and equally spaced in time intervals. A time series plot is a tool used in Six Sigma to depict an orderly sequence of values of a variable that frequently occur when looking at process or product monitoring and control. To better understand what this means, we must first gain a better understanding of what Six Sigma is.

Six Sigma is a methodology used in process improvement. It uses data and statistical analysis to identify and fix problem areas for any particular company. To do so, a problem or process is first identified and defined objectively. The process’s output performance is then measured. Next, the problem is analyzed in all phases of the process in order to identify causes of variation.

The result is that output processes can then be improved by reducing that variation. This improvement program can affect everyone involved in all processes including, but not limited to production and manufacturing, service and business processes.

The reason time series plots are used in Six Sigma is because it is necessary to know averages, peaks and down cycles. You want to try to understand any particular time series to understand the underlying theory of the data points like where they came from, what generated them, or to make forecasts and predictions.

A time series is a series of measurements of anything at all that are usually taken at regular intervals. Sometimes the intervals are not strictly regular such as calendar months, which are near enough for most purposes, or trading days which have weekends and public holidays missing. There are two different kinds of time series data:
1. Continuous: this is where you have an observation at every instant of time. Examples would include electrocardiograms and lie detectors. A continuous barograph trace is another example of a continuous time series.
2. Discrete: this is where you have an observation at regular, spaced intervals. A sequence of hourly pressures is an example of a discrete time series.
A time series graph shows the growth of a sequence as a function of time. By plotting data as a function of time, you can gain insight into several data features. These features, when considered in the context of the data, enable you to plan your analysis strategy. These features include:
• Outliers: values that do not appear to be consistent with the rest of the data
• Discontinuities: a break or gap in a process that would normally be continuous
• Trends: a general tendency in movement or direction
• Periodicities: any recurrence at regular intervals
• Time intervals containing the data of interest
When plotting your data, you should display the data in the order in which they arose. This is important if they are successive because they observations will probably be co-dependent. Time series are best displayed on a scatter plot. The vertical axis is where you will plot the series value X. The horizontal axis is where time (t) will be plotted. Time is often called the independent variable.
In summary, a time series plot is a valuable tool used in order to analyze time series or series of events and observations. It is important to gain insight into many processes.

Rating: 5/5 (1 votes cast)

---

Categories: Facilities Management, Postage, Project Management, Supply Chain Management, Total Quality Management,

Help others find this article:
Socializer, Digg, del.icio.us, reddit, StumbleUpon

Favorites: Add to favorites
Tags: time_series_plot time_series define_time_series_plot Six_Sigma