AI-driven operations forecasting in data-light environment

  • Choosing the right AI model. In many instances, machine-learning (ML) models can test and validate multiple models to find the optimal choice, with minimal human involvement.
  • Leveraging data-smoothing and augmentation techniques. This technique works when a period within a time series is not representative of the rest of the data.
  • Preparing for prediction uncertainties. Sophisticated scenario-planning tools that let people insert a wide range of parameters can help when forecasting models do not achieve satisfactory accuracy.
  • Incorporating external data APIs. This option is applicable when external data sources are necessary to inform the forecast values.

Choosing the right AI model

Having more historical data generally makes for more-robust forecasting. Long-running historical data are not available for all cases. In such situations, a successful forecast provides reasonable outputs for cases with a low sample size while maximizing the accuracy of outputs for cases with long-term historical data.

Leveraging data-smoothing and augmentation techniques

Often, time-series data are influenced by anomalous periods that disrupt overall trend patterns and make it extremely difficult for any AI model to learn and forecast properly. Smoothing is a technique to reduce the significant variation between time steps. It removes noise and creates a more representative data set for models to learn from.
The impact of smoothing 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.

Preparing for prediction uncertainties

Relying solely on statistical forecasts may not provide the business insight required. This is especially true for long-term forecasts, as unexpected events that affect trends and seasonality make it more difficult to learn from historical patterns. Given the inherent uncertainty of forecasting analysis in such cases, it is useful to use what-if scenarios.

Incorporating external data APIs

Externally sourced data can cover a variety of sources and content, including social-media activity, web-scraping content, financial transactions, weather forecasts, mobile-device location data, and satellite images. Incorporating these data sets can significantly improve forecast accuracy, especially in data-light environments. These sources provide an excellent option for the inputs required for AI-driven models and create reasonable outputs.



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David BECK

David BECK


AI • Blockchain — David is a former Entrepreneur, Contributor to French government publication. He is now a Teacher, Researcher, Expert at La (French) WineTech.