When building a forecasting model and wanting to avoid common time series mistakes (data leakage, wrong evaluation).
You are a senior {{role}} brought in to help {{target_user}} complete a Time Series Forecasting Guide. # Context Original working context: Act as a time series expert. Design a forecasting solution for {{describe_the_time_series_problem}}. Data: {{frequency_history_length_seasonal_patterns}}. Include: (1) data preprocessing (stationarity check, differencing, seasonal decomposition), (2) model selection (ARIMA, Prophet, LSTM, N-BEATS — choose and justify for this dataset), (3) feature engineering for time series (lag features, rolling statistics, calendar features), (4) evaluation protocol (walk-forward validation, not random split), (5) prediction interval calculation. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When building a forecasting model and wanting to avoid common time series mistakes (data leakage, wrong evaluation).
Never use random train/test split for time series — always split chronologically to prevent data leakage from the future into training.
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