Forecasting can be challenging. Why open yourself up to limitations and risks by relying solely on manual forecasting techniques?
Here are the top 3 limitations of only using Excel to get your forecasting done:
#1 Limited number of leading indicators you can test
It’s a numbers game. Why not get the highest-performing forecast by getting access to a large number of leading indicators?
The regression function built into Excel can only handle 16 variables. Using four lags for each variable limits the total number of leading indicators to only 3, as the main variable needs to be lagged as well.
Indicio can analyze up to 50 leading indicators, giving you the advantage of identifying the valid indicators among all the noise.
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#2 Rigid indicators
Are your leading indicators relevant to your forecasts, and how do you determine that?
One of the ways to do so is to conduct a quality check to minimize spurious correlations and wrong interpretations. Unfortunately, Excel does not offer the possibility to double-check this. With only rigid indicators in place, you could run the risk of generating an inaccurate forecast, leading to potential losses.
By employing automated forecasting based on the latest academic algorithms, you can quickly identify the specific indicators that enhance your forecast and rule out those that hinder better performances.
Learn more about how one of our customers went from only using the same five indicators to identifying the relevant indicators they should focus on. Here's how they did it.
#3 Limited to using a simple linear model
When making critical business decisions, you want to have a pool of models to capture and assess as many perspectives as possible.
With Excel, you only have a simple regression tool, and relying on one model’s insights presents a risk. Having access to a rich library of econometric and machine learning models allows you to quickly and accurately identify the model that offers the best performance at any horizon.
Indicio automatically applies a rich library of econometric and machine-learning models to identify the model offering the best performances at any horizon.
