An algorithm is a procedure or formula for solving a problem, based on conducting a sequence of finite operations or specified actions. Generally speaking, when most people talk about algorithms, they’re talking about a mathematical formula or something that is happening behind the scenes, like the operations that power our social media news feeds.

While these are indeed algorithms and they are a sequence of steps designed to perform a task, algorithms are more than just math.

An algorithm is any detailed operation used to carry out an operation or solve a problem and may be as simple and ‘non-mathy’ as the recipe to bake a cake.

How Algorithms Work In Forecasting

In demand planning, where the cake we are baking is a forecast, our recipe generally entails different prediction methods and approaches, along with layers built from inputs from various sources. The steps and sequence of the inputs, the configuration of the methods, the repeating of steps, and the outputs all come together to form an algorithm.

And this can easily consist of multiple methods and inputs reduced to three logical operations: AND, OR, and NOT. While these operations can chain together in extraordinarily complex ways, at their core, algorithms are built out of simple rational associations and a limited series of steps.

What this means is that an algorithm can be anything you like, for example, an exponential smoothing model that takes an input, uses a set of rules, parameters and steps to deliver an output to your forecasting process.

After you have properly defined the need and have the right data in the right format, you get to the predictive modeling stage which analyses different algorithms that to identify the one that will best future demand for that particular dataset.

The 6 Models Commonly Used In Forecasting Algorithms

Yet, while there are many different algorithms available to us, there’s a smaller set of fundamental predictive modeling techniques that are typically applied in forecasting, including the following:

Clustering analysis: This technique is a way to help understand and analyze data by putting it into smaller manageable subgroups to highlight attributes and manage or make better predictions. The resulting classification model can be used both to categorize new records and to do predictive modeling against the data for the designated subgroups.

Descriptive analysis: This helps tell you what has happened in the past and attempts to analyze and characterize it, with an eye toward predicting similar events in the future. Describing past behavior and then applying predictive models to the resulting data helps to frame opportunities for operational improvement and identify new business opportunities.

Outlier analysis: Detecting the outlying values in a dataset to identify noise and improve prediction and anomalies.  A database may contain data objects that do not comply with the general behavior or model of the data and may be isolated to better understand or determine impacts or calculated responses.

Factor analysis: This helps you understand relationships and dependencies between different data variables to predict how they’ll affect one another going forward. The information enables you to predict future developments related to the dependent variable based on what happens with related factors.

Time series analysis: looks at a collection of values observed sequentially over time and is used to perform time-based predictions. Assuming that past data patterns such as level, trend, and seasonality repeat this can create models using only of the data being forecasted to predict future patterns.

Regression analysis: This helps understand relationships and help predict continuous variables based on other variables in the dataset.  This technique is designed to identify meaningful relationships among data variables, specifically looking at the connections between a dependent variable and other independent factors that may or may not affect it.

We’ll be discussing predictive analytics and data science at IBF’s Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from May 6-8, 2019. Join us at Harrah’s Hotel in the heart of the city for 3 days of world-leading training, networking and socializing. Features special Data Science Workshop to help attendees leverage the latest forecasting techniques.