Companies missed the 2008 financial crises as they were not modeling the data at a daily level. It is quicker at reacting to level shifts and changes in trends as the data is being modeled daily vs waiting a week/month to observe the new data. Y ou would want to use daily data as financial forecasting is often quite inaccurate when they employ “ratio estimates”. Particular weeks of the month may have an identifiable pattern for build up in anticipation for pay schedules. Long weekends, Fridays before holidays on Monday, and Mondays following Friday holidays can be identified as important. Days of the month also can be identified due to pay schedule. Days of the week have different patterns which can be identified at this level. If a holiday has days 1,2,3 before the holiday as very large volume a daily model can forecast that while the weekly won’t be able year in and year out model and forecast that impact as the day of the week that the holiday occurs changes every year.ĭaily data is superior for short-term/medium tactical forecasting. We have seen the need to allocate the 53 rd week to a “non-player” week to make the data a standard 52 week period which is workable, but disruptive compared to daily data.Īdvantages – Weekly data can’t deal with holidays and their lead/lag relationships.
The number of weeks in a year is subject to change and creates a statistical issue due to the fact that every year doesn’t have 52 weeks. When you have very systematic cyclical cycles like “artic ice extents” that follow a rigid curve and not need for day of the week variations.ĭisadvantages – Floating Holidays like Thanksgiving, Easter, Ramadan, Chinese New Year change every year and disrupt the estimate for the coefficients for the week of the year impact which CAN be handled by creating a variable for each. Integrating Macroeconomic variables like Quarterly Unemployment requires an additional step of creating splines.Īdvantages – When you can’t handle the modeling process at a daily level you “settle” for this. We were asked to share our thoughts on advantages and disadvantages of forecasting at monthly vs weekly vs daily levels.Īdvantages – Fast to compute, easier to model, easier to identify changes in trends, better for strategic long term forecastingĭisadvantages – If you need to plan as the daily level for capacity, people and spoilage of product then higher levels of forecasting won’t help understand the demand on a daily basis as a 1/30 th ratio estimate is clearly insufficient.Ĭausal variables that change on a frequent basis (ie daily/weekly – price, promotion) are not easily integrated into monthly analysis