Understanding and Reducing Data Bias in ML Forecasting Models

A misstep in forecasting can result in millions lost by a business. Don’t let data bias be the cause of your forecasting errors

Machine learning (ML) algorithms have propelled demand forecasting efforts by rapidly detecting outliers and improving prediction efforts. Regardless of your algorithm’s complexity or functionality, if you are using inaccurate or unrepresentative data to train ML models, forecasting errors are inevitable. 

Machine learning data bias – also called AI bias –  occurs when an algorithm produces inaccurate results due to faulty assumptions and biases in the machine learning process. Data teams are tasked to tackle data bias in machine learning to prevent errors in demand forecasting models. But sometimes it’s challenging to pinpoint the type of bias and update your dataset, making it crucial to invest in intelligent, diverse data to mitigate bias risk. 

Demand intelligence provides additional insight your ML model needs to accurately predict future demand. Training your models with demand intelligence can help mitigate multiple types of data biases that ML models are exposed to.  

Types of data biases 

In general, training data for machine learning forecasts must be fully representative to ensure accuracy for the business. There are three types of data biases that your team must consider in order to decrease forecasting errors: sample bias, confirmation bias, and exclusion bias.

Sample bias 

Sample bias occurs when the data used to train forecasting models is not representative of the real life environment/real life scenarios.. For example, if your team is only using historical transaction data and seasonality trends, your model isn’t getting an accurate representation of all the other external factors impacting demand. PredictHQ aggregates, standardizes, and enriches millions of external demand data points that teams can access through the demand intelligence API. You can couple the API with your existing historical data to train your models and prevent sample bias. 

Confirmation bias 

Confirmation bias is the tendency to make decisions based on prior, unvalidated, beliefs or hypotheses. For example, coffee shops might be tracking business conferences in their area because they have found strong correlation between attendance and sales. However, teams aren’t tracking school holidays and university events because they don’t subjectively feel that there might be a strong correlation. With PredictHQ, you have instant access to 18+ event categories that gives you a large breadth of demand causal factor data. Now your models can be trained by a larger selection of data, reducing your forecasting error risk.

Exclusion bias 

Exclusion bias can happen when team members remove valuable data that was thought to be unimportant. It can also occur due to the systematic exclusion of certain information. For example, football games might not have had a significant impact on demand last season so the team decides to remove these data points from their model. With the large change in consumer behavior resulting from COVID-19, TV viewership might now have a much higher impact on your demand this season, but because of the exclusion bias your model won’t be able to learn from this.

PredictHQ ensures data quality and representation so you can eliminate data bias risks

Our data processing pipeline is built by data scientists, for data scientists to minimize data bias risk and improve forecasting accuracy. Through data diversity, bias testing, data labeling and feature engineering we’re able to guarantee high quality, unbiased data for your machine learning algorithms. 

  • Ensure data diversity: During our data quality process, we aggregate events and demand data points from hundreds of public and proprietary data sources to give us depth and diversity.   

  • Bias testing: Our Data Assurance team constantly assesses the quality of our data sources to ensure biases don't exist.  

  • Data labeling: PredictHQ has created NLP labels that ensure data labeling guidelines are clear and straightforward.  

  • Feature engineering: We’ve built features within our data so your models can quickly identify the demand causal factors with the highest business impact.

With demand intelligence, data teams can improve their time series forecasts and help the business make better decisions around product pricing, staffing schedules, inventory levels, and more. 

You can’t prepare for what you don’t see coming

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Knowing the impact of demand causal factors like events will transform your business. The American Society of Hematology has a $45M estimated economic impact — and that's only one event in one city.

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