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6 trend analyses to consider prior to time series forecasting

Posted by Bala Deshpande on Mon, Feb 24, 2014 @ 07:38 AM

(This article was contributed by Vaibhav Waghmare)

Analysis of historical data plays a vital role in business decisions. Often, data collected from past projects and investments can be useful in providing estimates for future projects.  Being able to analyze historical data and establish relationships between key variables enables us to have systematic and relevant information about business operations. This can be a significant competitive advantage as it enables a more structured approach towards decision making. 

When we perform a time series forecasting analysis, as described earlier, the methodology automatically generates trends, seasonal and/or cyclic patterns. Sometimes, understanding a basic trend is quite a valuable insight. The main visual in a dashboard built for one of our customers includes an overall trend chart from which the manager can get a quick 30,000 foot overview of the demand for their products. While sophisticated time series forecasting tools automatically extract the trends,sometimes it may be more intuitive to first examine the trends by themselves before reverting to more sophisticated time series modeling. Anyone who has used spreadsheets will quickly recognize this.

Following are different ways where your historical data may fit in:revenue trend lines time series forecasting data resized 600

Linear - A linear trend line is a best-fit straight line that is used with simple linear data sets. Your data is linear if the pattern in its data points resembles a line. A linear trend line usually shows that something is increasing or decreasing at a steady rate.

Logarithmic - A logarithmic trend line is a best-fit curved line that is most useful when the rate of change in the data increases or decreases quickly and then levels out. A logarithmic trend line can use negative and/or positive values.

Polynomial - A polynomial trend line is a curved line that is used when data fluctuates. It is useful, for example, for analyzing gains and losses over a large data set. The order of the polynomial can be determined by the number of fluctuations in the data or by how many bends (hills and valleys) appear in the curve. An Order 2 polynomial trend line generally has only one hill or valley. Order 3 generally has one or two hills or valleys. Order 4 generally has up to three.

Power - A power trend line is a curved line that is best used with data sets that compare measurements that increase at a specific rate — for example, the acceleration of a race car at one-second intervals.

Exponential - An exponential trend line is a curved line that is most useful when data values rise or fall at increasingly higher rates. You cannot create an exponential trend line if your data contains zero or negative values.

Moving average - A moving average trend line smoothes out fluctuations in data to show a pattern or trend more clearly. A moving average trend line uses a specific number of data points (set by the Period option), averages them, and uses the average value as a point in the trend line. If Period is set to 2, for example, then the average of the first two data points is used as the first point in the moving average trend line. The average of the second and third data points is used as the second point in the trend line, and so on.

different trends in time series forecasting data resized 600

Download a free case study to understand how a time series forecasting driven dashboard is used by small businesses

production planning using demand forecasting - case study cover

Topics: business analytics, multiple linear regression, time series analysis

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