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Implementing Semantic Abstraction with GPT-5 Pro

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Linked challenge: Adaptive LLM Time Series Forecasting with DSPy & W&B

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Linked challenge

Adaptive LLM Time Series Forecasting with DSPy & W&B

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Develop a module that generates 'semantic abstractions' from raw time series data and associated contextual information (e.g., event logs, market news). Utilize GPT-5 Pro (or an equivalent model via Together AI) with DSPy to prompt the LLM to identify trends, seasonality, anomalies, and potential causal factors, expressing them in natural language. The output should be a structured textual summary that can guide subsequent forecasting. Focus on creating robust DSPy programs that reliably extract these insights even with noisy data.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Adaptive LLM Time Series Forecasting with DSPy & W&B

This challenge focuses on building an advanced time series forecasting system that leverages the power of Large Language Models (LLMs) like GPT-5 Pro. Instead of traditional statistical or deep learning models, you will design a system where an LLM performs forecasting by first generating "semantic abstractions" of the time series data (e.g., identifying underlying trends, seasonality, significant events, and causal factors from textual descriptions or metadata). These abstractions will then guide the LLM's forecasting process, allowing for more interpretable and adaptable predictions. The system should also incorporate principles of continuous learning, demonstrating how the forecasting model can adapt to new data patterns or updated domain knowledge without requiring full retraining from scratch. You will use DSPy to programmatically optimize the LLM's prompting and reasoning steps for forecasting and integrate Weights & Biases for experiment tracking, model evaluation, and monitoring the adaptive learning process. Apache Airflow will orchestrate the data ingestion, semantic abstraction generation, forecasting, and adaptation pipeline.

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