# Choosing the right forecast period

Forecasting consists in producing figures that are supposed to reflect the future. But those figures depend heavily on the **period** chosen for data aggregation. Lokad supports the most frequently used periods: quarter-hour, half-hour, hour, day, week, month, quarter, semester, year …

Intuitively, **the longer the considered period, the easier it is to make an accurate forecast**. For example, yearly forecasts eliminate seasonal variations. Although a short forecasting period might provide a false sense of accuracy (ex: forecasting daily candy sales over the next two years) whereas a large period might be unsuited to take operational decision (ex: trying to optimize the weekly worker schedules of the candy manufacturing unit based on yearly forecasts).

**A careful choice of the forecasting period is essential to make the most of forecasting.** Yet, surprisingly, this question is frequently left mostly unanswered in books treating the subject of *forecasting for practitioners* (usually focusing on sales or demand forecasting). Typical answers are *most of the manufacturing industry is using monthly forecasts* and *many large retailers are using weekly forecasts*.

Yet, simple assumptions can lead to practical quantitative clues to make this choice. If we just assume that forecast errors follow a normal distribution, then expected error increase when switching to a shorter period is

- year → month: √(
^{12}⁄_{1}) ≈ 3.5 (i.e. error multiplied by 3.5) - month → week: √(
^{31}⁄_{7}) ≈ 2.1 (assuming a month with 31 days) - week → day: √(
^{6}⁄_{1}) ≈ 2.5 (assuming 6 business days per week) - hour → quarter-hour: √(
^{4}⁄_{1}) = 2

Although, the normal distribution assumption is usually not exactly verified, those figures are quite representative of most situations. Those figures can be used to evaluate the opportunity to change the forecasting period if the forecast error is too high or if the forecast period is too long.