You are considering an LSTM for a prediction problem with sequential observations. Explain what an LSTM is designed to capture, how you would prepare and validate the data, and how you would determine whether it is preferable to simpler sequence baselines. ### Constraints & Assumptions - Sequence lengths may vary and later observations must not leak into earlier predictions. - Compute and labeled data are limited enough that model complexity matters. - The task could be sequence classification, forecasting, or next-step prediction; clarify which before proposing details. ### Clarifying Questions to Ask - What is one sequence, what is one time step, and what target is predicted at what horizon? - Are observations regularly spaced, and how should missing steps be represented? - How much history is available, and which dependencies are expected to matter? - Is real-time inference, interpretability, or uncertainty estimation required? ### What a Strong Answer Covers - The gated recurrent structure and its purpose in retaining and updating information across time. - Correct construction of sequences, masks or padding, features, targets, and time-respecting validation. - Baselines such as last value, seasonal rules, feature-based regression, or a simpler recurrent model. - Evaluation by horizon and segment, with safeguards against leakage and overfitting. - A balanced comparison with other models based on data size, dependency length, latency, and maintainability. ### Follow-up Questions 1. How can padding affect training, and how would you prevent padded steps from influencing results? 2. What symptoms would suggest the model is overfitting? 3. When might engineered lag features outperform an LSTM? 4. How would you handle a distribution shift in sequence length?