The 7 Step Guide to Prompt Engineering for Time Series

Master Time Series Analysis with this advanced Prompt Engineering framework! 📈

Are you still relying solely on traditional statistical models, such as ARIMA or SARIMA, for your forecasting?
This video details the 7 steps of prompt engineering for time series analysis, emphasizing the importance of understanding temporal context, feature extraction, and anomaly detection. It demonstrates how to effectively combine llm reasoning with classical statistics models to improve forecasting techniques and gain deeper data science insights. The presentation highlights the critical role of domain knowledge in contextualizing temporal structure and leveraging machine learning algorithms for more meaningful results. We explore the "Hybrid Workflow"—combining the mathematical precision of traditional statistics with the semantic power of AI. Whether you are in Banking, Public Health, or Weather Forecasting, these strategies will help you build more robust, explainable, and localized AI systems.

In this video, you will learn:

How to contextualize temporal data for better AI interpretation.

The secret to "Hybrid Workflows" (LLMs + Traditional Stats).

Why domain knowledge is the "Expert Role" your AI needs.

The ethics of data privacy when uploading CSVs to LLMs.

Real-world applications in Public Health and Global South localization.

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Timestamps
00:00 – Introduction to Time Series Prompt Engineering

00:42 – Feature Extraction: Identifying Peaks and Seasonality

01:14 – The Hybrid Workflow: Combining LLMs with Statistics

01:42 – Explainable AI (XAI) in Public Health & Medicine

02:33 – Solving Training Bias: Localizing AI for the Global South

03:36 – Personalized Medicine vs. Generic AI Recommendations

05:05 – Why Traditional Models (ARIMA/SARIMA) Still Matter

05:33 – Structured Data: Using JSON for Garbage In, Garbage Out Prevention

06:33 – The Power of Domain Knowledge and Expert Roles

07:41 – Deep Dive: Transformer Architecture and Attention Mechanisms

08:55 – AI as your Research Assistant: The Supervisor Role

14:13 – Defining Prompt Engineering for Accurate Outputs

16:26 – Recap: Limitations of Traditional Moving Averages

20:26 – The Value of Information Over Time (Social Media Trends)

27:05 – Case Study: Historical Evolution and Socio-Political Trends

31:13 – 7 Guides to Prompting Time Series (Step-by-Step)

33:45 – Practical Example: Forecasting 365 Days of Sales Units

42:51 – Critical Alert: Data Privacy and Ethics in AI

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