Time series forecasting transforms historical patterns into actionable predictions, but the real challenge is balancing accuracy with explainability. Neural networks deliver precision but lack transparency; statistical models offer clarity but sacrifice complexity.

This DI Squared whitepaper reveals how to navigate this trade-off and build forecasts that drive business results. Gain practical guidance on ARIMA/SARIMAX versus N-BEATS, TCN, and BlockRNN, the critical role of covariates, and production-ready back-testing strategies.

Learn when to start simple and when to add complexity, so your forecasts deliver both trustworthy insights and measurable impact.