Machine Learning in dynamic, adversarial environments

Forecasting the future is the essential business problem - it's also the hardest one! Applying Machine Learning naively will create suprises along the way.

We specialize in modeling non-stationary time series, where the underlying process that generates the data changes over time. Like in all Complex Dynamical Systems.

Applying machine learning to non-stationary time series forecasting requires careful consideration and domain expertise. Naive application of machine learning algorithms can often result in unexpected outcomes or false conclusions. This is because machine learning models are typically overfitted on a given dataset, but may not perform well when deployed in the real world, where the underlying processes may be changing over time.

To successfully apply machine learning to non-stationary time series forecasting, it's important to use appropriate techniques and algorithms that can adapt to changing underlying processes. This requires a deep understanding of the domain and the factors that influence the time series data.

One approach to modeling non-stationary time series is to use dynamic models, which incorporate the changing factors into the forecasting model. These models can capture the complexity and dynamics of the underlying processes and provide accurate predictions even in the face of changes.

Another important consideration when applying machine learning to non-stationary time series forecasting is the need for ongoing model evaluation and refinement. This involves monitoring the model's performance over time, identifying any changes in the underlying processes, and adapting the model as necessary to ensure continued accuracy and reliability.

Ultimately, the key to successful forecasting using machine learning is to combine advanced algorithms and techniques with deep domain expertise and ongoing monitoring and refinement. By doing so, organizations can gain valuable insights into future trends and events, and make informed decisions that drive business success.