Data & Analytics

Processes long sequences in fixed-size chunks while carrying RNN hidden state across chunks for memory-efficient inference.

Detect and mask outlier data points using iterative sigma-clipping on reference frames or calibration data

EDA visualization of multi-modal sensor data with axvspan shading for labeled behavioral phases and contiguous span detection

Scaled Pinball Loss (SPL) metric for evaluating quantile forecasts, normalized by mean absolute successive differences of training data

Walk-forward validation for ARIMA by refitting on history at each step, forecasting one step ahead, then appending the true observation — produces an honest one-step error distribution that mirrors nightly-retrained production forecasters

Forecast a multi-step horizon by predicting one day ahead, writing the prediction back into the panel as the new "actual", recomputing all lag and rolling features that depend on it, then predicting the next day — turns a one-step LightGBM regressor into a 28-day forecaster without changing the model

Fill gaps in a daily exogenous series (oil prices, sensor feeds) by merging against a full calendar to expose NaNs, then replacing each NaN with the midpoint of its nearest valid left and right neighbors, walking outward past consecutive NaN runs

Estimate periodicity from irregularly sampled multi-band time series using the multiband Lomb-Scargle periodogram, then phase-fold observations

Compute log-ratio features between adjacent frequency bands as color indices to characterize spectral shape from multi-band time series

Generate lag features for multiple targets over N days by shifting evaluation dates and self-joining per entity, creating a wide feature matrix of past target values

Build a single per-row "day off" boolean from a holidays table with National/Regional/Local locale hierarchy and Work Day overrides that flip make-up working weekends back to working days

Single neural network outputting all quantiles simultaneously via pinball loss over a quantile vector for joint probabilistic forecasting

Negative log-likelihood loss over K isotropic-Gaussian trajectory modes with per-mode confidences and logsumexp stability

Evaluate probabilistic forecasts using normalized Gaussian log-likelihood relative to naive and oracle baselines, scoring both mean accuracy and uncertainty calibration

Engineer SNR-derived features from irregular time series — flux ratio squared, error-weighted mean flux, and normalized amplitude/range features

Walk-forward stacking ensemble that trains base models on expanding windows and a meta-learner on their out-of-fold predictions across time

Detects discrete events (state transitions) from continuous predictions using local maxima with minimum-interval constraints.

Multi-step detector calibration pipeline — ADC inversion, hot/dead pixel masking, nonlinearity correction, dark subtraction, flat-field normalization

Custom multiclass log-loss that weights per-class contributions by class frequency and domain importance, usable as both training loss and eval metric

Generate prediction intervals by repeatedly sampling from model residuals, adding to point forecasts, and taking quantiles across synthetic futures

Multiply per-timestep regression loss by a 0/1 availability mask so missing future steps contribute zero gradient

Override model predictions with last known value for low-activity or low-density entities where learned trends are unreliable

Split a multi-year table into per-year partitions, run the same groupby aggregation on each, then concat and gc — a pure-pandas map-reduce that survives 100M+ rows on a 16GB kernel

Use XGBoost DeviceQuantileDMatrix with a custom batch iterator to train on large datasets without exhausting GPU memory