  Machine learning for time-series forecasting remains a key area of research.
Despite successful application of many machine learning techniques, relating
computational efficiency to forecast error remains an under-explored domain.
This paper addresses this topic through a series of real-time experiments to
quantify the relationship between computational cost and forecast error using
meteorological nowcasting as an example use-case. We employ a variety of
popular regression techniques (XGBoost, FC-MLP, Transformer, and LSTM) for
multi-horizon, short-term forecasting of three variables (temperature, wind
speed, and cloud cover) for multiple locations. During a 5-day live experiment,
4000 data sources were streamed for training and inferencing 144 models per
hour. These models were parameterized to explore forecast error for two
computational cost minimization methods: a novel auto-adaptive data reduction
technique (Variance Horizon) and a performance-based concept drift-detection
mechanism. Forecast error of all model variations were benchmarked in real-time
against a state-of-the-art numerical weather prediction model. Performance was
assessed using classical and novel evaluation metrics. Results indicate that
using the Variance Horizon reduced computational usage by more than 50\%, while
increasing between 0-15\% in error. Meanwhile, performance-based retraining
reduced computational usage by up to 90\% while \emph{also} improving forecast
error by up to 10\%. Finally, the combination of both the Variance Horizon and
performance-based retraining outperformed other model configurations by up to
99.7\% when considering error normalized to computational usage.
