Rectifier layer

Boost Your Forecaster

RectifierML adds a model-agnostic reliability layer to advanced forecasting systems, helping reduce costly forecast mistakes before they hit your grid or portfolio.

Electricity demand forecast

RectifierML corrects forecast while retaining the original model identity.

Electricity Forecasting

Day-ahead demand forecasting across peaks, ramps, and low-load periods. RectifierML reduces forecast error after the baseline model prediction.

Open-access load data

What the electricity dataset represents

Day-ahead load forecasting evaluated across peak periods, ramp transitions, and low-load intervals.

Forecast target Demand level and peak-risk behavior
Base models TFT, N-BEATS, and XGBoost

Methodology Evaluation

Original MAE, rectified MAE, and signed change appear after Rectify Now.

Investment Forecasting

Return and volatility forecasting for decision-sensitive financial time series. RectifierML improves forecast reliability after the baseline model prediction.

Open-access market data

What the finance dataset represents

Return and volatility forecasting evaluated under decision-sensitive market conditions.

Forecast target Return direction and volatility risk
Base models LSTM, LightGBM, and ARIMA

Methodology Evaluation

Original MAE, rectified MAE, and signed change appear after Rectify Now.

Improved forecasts without replacing the base model.

How it works

A forecasting model first produces the baseline prediction. RectifierML then refines that output through an additional reliability layer, returning a corrected forecast with a calibrated 95% safety margin for decision-making.

Contact Person mgian@ics.forth.gr

For technical details, partnership, or pricing, feel free to get in touch directly.