Forecasting vs. Annualization: Determining the Right Approach at the Right Time

Reporting specialists and data analysts are often required to predict the future for stakeholder groups. They do this through a variety of models, including forecasting and annualization. Although both methodologies aim to predict future values, their applications and the mathematical logic behind them vary significantly, catering to different business needs.

What is Annualization and Its Significance?

Annualization is a mainstay for finance and accounting but there are situations where it may be useful in HR contexts. It can be used to estimate year-end values for turnover rates, total new hires, and job openings filled, based on current data.

Annualization works well when:

  • There is little volatility in the metric across time periods
  • There is little seasonality in the metric
  • The metric is not likely to trend upward or downward during the course of the year

Simplify Annualization with One Model

One Model streamlines the computation of annualized metrics. By selecting the "Year to Date" option and "Annualize" in "Time Functions," the system will only consider the current year's data, offering a clear example of annualization at work.


The Case for Forecasting

Forecasting provides several benefits over annualization. While annualization typically only considers data points from the current year, forecasting can:

  • Utilize data from a much wider time frame and range of data points
  • Factor in seasonal fluctuations and trends
  • Provide a more nuanced view of potential future states with confidence intervals, which is especially valuable for HR metrics that exhibit variability (e.g., number of hires, number of terminations, and termination rates).

Simplify Forecasting With One Model

One Model simplifies forecasting with its Embedded Insights feature. Just create a time-series line graph for your metric and use the feature to extend your forecast to the year's end. Increasing the number of data points, by adjusting the time metric from monthly to weekly or daily, for instance, can enhance forecast accuracy by capturing shorter-term cycles that may be present in the data. Including data from at least 30 data points will improve the accuracy of your forecasts and if annual seasonality is present, including data covering two or more years will also improve accuracy.

You can adjust forecast parameters to align the final forecast period with the year-end. After running the forecast, simply click on the last data point in the visualization to see the forecasted value and its confidence interval. For more complex situations where the current year data pattern is expected to shift relative to last year’s pattern, One AI can be used to create a predictive model that incorporates additional internal and external features to improve accuracy.

Making the Choice: Annualizing or Forecasting?

Annualization and forecasting each have their strengths and weaknesses. Deciding between them depends on your data and your stakeholders’ needs. Sometimes a rough approximation is good enough; other times, a precise estimate or a range of values (e.g., a confidence interval) will be required.

Annualization Forecasting
Only considers data from the current year Can leverage data from multiple years
Only needs a single month of data to start the estimation process One Model will need at least 4 data points to produce a forecast, but forecast accuracy suffers with so few data points unless the metric progresses in a very linear fashion
Does not adjust for seasonality or trend Accounts for trends and seasonality
Very simple approach requiring little input regarding computations and easy to understand More sophisticated approach that may prompt questions from end-users (luckily One Model provide embedded information describing the forecast)
Estimates made early in the year are likely to be inaccurate Estimates made early in the year are likely more accurate than Annualization, especially when data from the prior year are utilized
Will always underestimate or overestimate if a trend is present Can produce more accurate results even when trend is present


Alternatives and Strategic Adjustments

Alternatives like the 12-month rolling average provide another strategy for estimating year-end values, accommodating changes anticipated over the year. For specific metrics, like annual turnover, manually adjust the year-end prediction using expert analysis or the expected effects of internal actions.

Depending on the metric being forecasted, it may also be reasonable to manually adjust the year-end value based on general projections for the year. For instance, to predict next year's annual turnover, start with the current end-of-year rate and refine it using projections from external experts or by considering the expected effects of internal measures aimed at reducing turnover.

One Model Simplifies Forecasting and Annualization

You might encounter scenarios where estimating year-end values for a metric is necessary. Although predicting these values with absolute precision is challenging, One Model can generate reasonable estimates, bearing in mind that sudden changes mid-year could significantly affect forecast accuracy.

In practice, forecasting, particularly with One Model's Embedded Insights, tends to be more effective than annualization, especially at the start of the year. However, the accuracy of forecasting is impacted by decisions related to data inclusion and model parameters. Forecasting may also require a bit more effort to maintain, albeit minimal.

Fortunately, One Model simplifies the use of both annualization and forecasting. In fact, using both methods to create estimates can be practical. When the results are close, opting for the annualized figure might be preferable for its simplicity. If results differ, the underlying data should be evaluated and the method that best aligns with the data’s characteristics should be used.


One Model has you covered regardless of the situation you face and the approach you prefer or choose.

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Written By

Steve loves data and loves to solve problems, which makes him a great fit as One Model’s Solution Architect. With a background in Industrial/Organizational Psychology, Steve has done work in talent management, employee engagement, program evaluation, organizational research, and just prior to joining One Model, he was leading a People Analytics team. Steve would say that every dataset has a story to tell and it’s our job to pull that story out. When he’s not playing with data, Steve and his son do tournament bass fishing in Central Florida.

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