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AI In Forecasting

AI In Forecasting

What Is Business Forecasting?  

 Before we get into the application of AI and ML, let’s understand first what is business forecasting and how it works. It can be defined as a process of using time series data for the estimation and prediction of future developments in areas such as sales, revenue, and demand for resources and inventory. In particular, business forecasting is generally divided into the categories of Demand Forecasting and growth Forecasting. Forecasting demand for resources such as inventory and staff while growth Forecasting is an accurate forecast of future growth.

How Does It Work?  

An AI (Artificial Intelligence) based forecasting framework is used to ensemble machine learning (ML) algorithms to optimize the forecasts that are being done. The tech system via its software then selects a model that is uniquely designed for a particular enterprise that you’re forecasting. A turn-key solution does all this without the help of manual input since it independently manages the entire ML pipeline right from the stage of training to the model and tuning hyper-parameters to the stage of deployment of the forecasting tools of production. In a nutshell, there is a step-wise overview of the process that an AI-based forecasting tool uses. They are explained in detail below along with graphics.

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Step 1: Connect the data: This provides the internal data sources as well as external data also that might be beneficial to the forecasting system.

Step 2: Forecast metrics selection: The customer then selects which metrics to forecast and what the time horizon should be.

Step 3: Preparation of automatic data: Data preparation is a significant stage in the entire process whereby an automated data preparation solution, the client is able to preprocess the historical data collected with the help of various algorithms so as to remove factors that aren’t relevant to the forecast.

Step 4: Train the ML model: Training the model ML involves sending the preprocessed data to a variety of algorithms to forecast and then testing the accuracy of each output/result.

Step 5: Creation of a customized model: Based on the accuracy of each model, only the best performers are screened so as to create a custom model for the forecasting task required to make for the client.

Step 6: Review the model: After the initial customized model is in the creation mode, we then conduct frequent reviews of the accuracy and repeat the previous training process if required.

Step 7: Making forecast: Next, we use the same custom model and the data provided to make a real-time data forecast and the results are stored for future use.

Step 8: Analyze forecast insights: This is the last stage where the forecasts are displayed on the dashboards and the reports are updated accordingly so that anyone can easily make use of such regardless of their technical or analytical expertise.

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Now that we have a high-level overview of ML for business forecasting, let’s review leveraging data and its augmentation technical know-how.

Leveraging Data-smoothing And Augmentation Techniques  

It is noticed often that the time-series data are influenced by anomalous periods which generally disrupt the overall trend patterns and make it extremely difficult for any AI model to learn and forecast properly. Smoothing is one of the techniques to reduce the significant variation between time steps. It removes the elements of noise and creates a representative for models to learn from. Its impact becomes more evident when the time-series data are affected by a particular event in the past that is not expected to recur regularly in the future.

Any company has its goal to forecast sales in its retail stores. Although the drop in sales volume during April and May seems to be a single event, it is majorly affected by the ML process. This period has completely different patterns of trend as compared to the time series. But the ML models do not automatically treat this period as anomalous. Instead, it takes learning from it alongside the rest of the time series since they are generalized with the overall patterns. In the graphics below, the anomalous period confused the model, and it cannot learn the intrinsic seasonality patterns as expected.


Summary: Business Forecasting With AI  

As we’ve discussed, whether it’s for growth or demand, business forecasting is a critical part of corporate planning. While many companies have traditionally used statistical techniques such as univariate and multivariate models, the reality is that these models simply can’t handle the number of business metrics and KPIs that companies have available for forecasting. Instead, an AI-based solution can take in as many factors that are available and produce a significantly more accurate forecast by identifying patterns and correlations that would have otherwise gone unnoticed. Not only can an AI-based solution take in all these factors, but it also requires minimal input from the user. With just the metrics to be considered, the time horizon and desired forecast, a turn-key AI-based forecasting solution can handle the entire pipeline from data preprocessing all the way to consuming the insights. As a result, regardless of the industry or what KPI is being forecasted, it seems that many companies are starting to understand that the accuracy of AI-based business forecasting gives early adopters a considerable advantage over the competition.

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