Build a time-series forecasting model

Q: Build a time-series forecasting model

This is a Machine Learning interview question from Jane Street for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

Forecast the Next H Steps for Time Series

Context

You are given one or more time series with timestamps and numeric targets (e.g., demand, returns, sensor values). Your task is to forecast the next H time steps. Some covariates may be available, including both observed-past features and known-future features (e.g., holidays, schedules, prices).

Requirements

Preprocessing
- Resampling and alignment across all series and covariates
- Timezone and DST handling
- Missing-data imputation strategy (short vs. long gaps)
- Outlier detection and treatment strategy
- Detrending and seasonality decomposition
- Scaling/normalization
Modeling (propose at least two approaches)
- One classical (e.g., ARIMA/SARIMAX/ETS)
- One deep learning (e.g., LSTM/TCN/Transformer)
- Justify: loss choice (MAE/RMSE/Huber/Quantile), multi-step strategy (recursive/direct/multi-output), and how to incorporate covariates (holidays, known-future features)
Evaluation (non-leaky)
- Walk-forward validation/backtesting protocol
- Metrics: MAE, RMSE, sMAPE/MAPE, and quantile loss for P50/P90
Monitoring and maintenance
- How to detect concept drift
- How to recalibrate or retrain
Implementation
- Provide concise pseudocode for training and inference

Build a time-series forecasting model

Forecast the Next H Steps for Time Series

Context

Requirements

Solution (Locked)

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