Predict future time-series values

Q: Predict future time-series values

This question evaluates proficiency in multi-step time-series forecasting, including data preparation with missing values and exogenous covariates, model architecture selection, implementation of training and windowing pipelines in PyTorch, and evaluation strategies such as backtesting and time-aware validation.

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Question

End-to-End Time-Series Forecasting (PyTorch)

Context

You are given one or more regularly sampled numeric time series and optional exogenous covariates (calendar features, prices of related assets, etc.). The goal is to forecast the next H time steps.

Assume:

Input time steps are ordered and may contain missing values/outliers.
Forecast horizon H and input context length L are to be chosen by you.
You should avoid data leakage and use time-aware validation.

Task

Design and implement an end-to-end forecasting solution:

Choose and justify a model architecture suitable for multi-step forecasting.
Implement the training loop in PyTorch, including dataset/windowing and evaluation.
Explain data preparation, hyperparameters, and design decisions.
Describe an evaluation strategy (splits, baselines, metrics, and backtesting).

Predict future time-series values

End-to-End Time-Series Forecasting (PyTorch)

Context

Task

Solution

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