Linear Trend

A linear pattern is a continuous decrease or increase in numbers over time. On a graph, this data appears as a straight line angled diagonally up or down (the angle may be steep or shallow). So the trend either can be upward or downward.

Exponential Trend

This technique produces non linear curved lines where the data rises or falls, not at a steady rate, but at a higher rate. Instead of a straight line pointing diagonally up, the graph will show a curved line where the last point in later years is higher than the first year, if the trend is upward.

Damped Trend

In this analysis, the line is curved line to show data values rising or falling initially, and then showing a point where the trend (increase or decrease) stops rising or falling.

Seasonality

One can identify a seasonality pattern when fluctuations repeat over fixed periods of time and are therefore predictable and where those patterns do not extend beyond a one year period. Seasonality may be caused by factors like weather, vacation, and holidays. It usually consists of periodic, repetitive, and generally regular and predictable patterns. Seasonality can repeat on a weekly, monthly or quarterly basis.

Irregular/Random Patterns

This type of analysis reveals fluctuations in a time series. These fluctuations are short in duration, erratic in nature and follow no regularity in the occurrence pattern. In prediction, the objective is to “model” all the components to some trend patterns to the point that the only component that remains unexplained is the random component.

Stationary/Stationarity

A stationary time series is one with statistical properties such as mean, where variances are all constant over time. A stationary series varies around a constant mean level, neither decreasing nor increasing systematically over time, with constant variance.

Cyclical Patterns

Cyclical patterns occur when fluctuations do not repeat over fixed periods of time and are therefore unpredictable and extend beyond a year.

In this article, we have reviewed and explained the types of trend and pattern analysis. Every dataset is unique, and the identification of trends and patterns in the underlying the data is important. If a business wishes to produce clear, accurate results, it must choose the algorithm and technique that is the most appropriate for a particular type of data and analysis. For example, the decision to the ARIMA or Holt-Winter time series forecasting method for a particular dataset will depend on the trends and patterns within that dataset.

A basic understanding of the types and uses of trend and pattern analysis is crucial, if an enterprise wishes to take full advantage of these analytical techniques and produce reports and findings that will help the business to achieve its goals and to compete in its market of choice.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include assisted predictive modeling, smart data visualization, self-serve data preparation and clickless analytics with natural language processing (NLP) for search analytics. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Research Market Guide to Self-Service Data Preparation, as a Niche BI and Analytics Vendor in the Gartner Report, Competitive Landscape in the BI Platforms and Analytics Software, Asia/Pacific, as a Representative Vendor in the Gartner Market Guide for Enterprise-Reporting-Based Platforms, and a Listed Vendor in the Other Vendors to Consider for Modern BI and Analytics, Gartner Report.

Original Post: What Are Data Trends and Patterns, and How Do They Impact Business Decisions?