Author: dotData

New automated feature engineering tools remove the need to choose between accuracy and interpretability. Revenue and demand forecasting are among the most common use cases in data science, with abundant available data and clear business value across multiple industries. However, little agreement remains about the ‘best’ approach for building such forecasting models. New automated feature engineering tools are making that debate less relevant. Drawbacks of Traditional Forecasting Solutions Algorithms such as ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal Autoregressive Integrated Moving Average), or XGBoost, remain popular due to their flexibility and interpretability. In a search for superior predictive performance, practitioners have been learning and incorporating an ever-expanding catalog of AI-based neural network architectures into their forecasting. Despite often outperforming classical approaches in forecasting accuracy, neural networks are not a one-size-fits-all solution. Neural networks are mostly uninterpretable, leaving the business with limited insights into what’s driving the forecast, limiting business impact…