Seasonality and trend analysis provides insight into historic behaviors. The analysis helps you understand different seasonal demand, spend and buying cycles. Trend analysis shows yearly and quarterly trends. 

PlanIQ first removes the trend components from the data and then identifies the seasonality. 

Seasonality analysis

PlanIQ identifies repeated performance. It checks for seasonal patterns on weekly, monthly or yearly timescales. 

For example, to work out a cycle for a typical day, PlanIQ looks at the performance data for each Wednesday of the year. 

PlanIQ identifies seasonality patterns based on the historical data timescale:

Time scaleWeekly seasonalityBi-weekly seasonalityMonthly seasonalityQuarterly seasonalityYearly seasonality




Here's the minimum amount of historical data you need for each type of seasonality analysis:

Time scaleWeekly seasonalityBi-weekly seasonalityMonthly seasonalityQuarterly seasonalityYearly seasonality
Daily5 weeks6 weeks3 months


3 months3 quarters3 years

3 quarters3 years

Even with postive historic values, you can get negative values in your seasonality analysis. This is due to how the analysis is carried out:

  1. The trendlines are subtracted from the historic data
  2. Then the seasonality data is calculated from the remaining historic data points, which can produce some negative seasonality points. 

If PlanIQ cannot identify any seasonal patterns, it can return noisy values. 

Tip: The more historical data you provide, the more accurate your seasonality analysis. 

Trend analysis

Trend is calculated as a moving average performed over a period of time. In PlanIQ:

  • Quarterly trends are calculated over the last 91 days
  • Yearly trends are calculated over the last 365 days

For trend analysis to happen:

  • You need at least 50% of filled values of historical data for a set period.
    If more than 50% of historical data is missing then it's not possible to calculate trends for the item for that period. 
  • Items with a shorter history or new to the dataset do not produce trend data .
    Over time, this changes as more historic values for these items comes into the dataset.

Trend analysis is lagging

To calculate quarterly trends, you need to provide the previous 3 months of historic data.

To calculate a yearly trend for 2021, you need to provide the previous 12 months of historic data.
For example, to calculate the yearly trend for March, provide data from March 2020 to March 2021

Find out how to include seasonality and trend analysis in your forecast results.  


We update Anapedia regularly to provide the most up-to-date instructions.