Prompt Engineering for Time-Series Analysis with Large Language Models

information is usually totally different from common evaluation, primarily due to challenges concerning the time-dependency that each information scientist ultimately runs into.

What in case you may pace up and enhance your evaluation with simply the proper immediate?

Massive Language Fashions (LLMs) are already a game-changer for time-series evaluation. For those who mix LLMs with sensible immediate engineering, they’ll open doorways to strategies most analysts haven’t tried but.

They’re nice at recognizing patterns, detecting anomalies, and making forecasts.

This information places collectively confirmed methods that go from easy information preparation all the best way to superior mannequin validation. By the top, you’ll have sensible instruments that put you a step forward.

Every thing right here is backed by analysis and real-world examples, so that you’ll stroll away with sensible instruments, not simply concept!

That is the primary article in a two-part collection exploring how immediate engineering can increase your time-series evaluation:

• Half 1: Prompts for Core Methods in Time-Sequence (this text)

• Half 2: Prompts for Superior Mannequin Improvement

👉 All of the prompts on this article can be found on the finish of this text as a cheat sheet 😉

On this article:

1. Core Immediate Engineering Methods for Time-Sequence
2. Prompts for Time-Sequence Preprocessing and Evaluation
3. Anomaly Detection with LLMs
4. Characteristic Engineering for Time-Dependent Knowledge
5. Immediate Engineering cheat sheet!

1. Core Immediate Engineering Methods for Time-Sequence

1.1 Patch-Based mostly Prompting for Forecasting

Patch Instruct Framework
A very good trick is to interrupt a time collection into overlapping “patches” and feed these patches to an LLM utilizing structured prompts. This method known as PatchInstruct may be very efficient and it retains accuracy about the identical.

Instance Implementation:

## System  
You're a time-series forecasting knowledgeable in meteorology and sequential modeling.  
Enter: overlapping patches of measurement 3, reverse chronological (most up-to-date first).  

## Consumer  
Patches:  
- Patch 1: [8.35, 8.36, 8.32]  
- Patch 2: [8.45, 8.35, 8.25]  
- Patch 3: [8.55, 8.45, 8.40]  
...  
- Patch N: [7.85, 7.95, 8.05]  

## Activity  
1. Forecast subsequent 3 values.  
2. In ≤40 phrases, clarify latest pattern.  

## Constraints  
- Output: Markdown checklist, 2 decimals.  
- Guarantee predictions align with noticed pattern.  

## Instance  
- Enter: [5.0, 5.1, 5.2] → Output: [5.3, 5.4, 5.5].  

## Analysis Hook  
Add: "Confidence: X/10. Assumptions: [...]".

Why it really works:

• The LLM will discover short-term temporal patterns within the information.
• Makes use of fewer tokens than uncooked information dumps (so, much less value).
• Retains issues interpretable as a result of you’ll be able to rebuild the patches later.

1.2 Zero-Shot Prompting with Contextual Directions

Let’s think about you want a fast baseline forecast.

Zero-shot prompting with context works for this. You simply give the mannequin a transparent description of the dataset, frequency, and forecast horizon, and it may possibly determine patterns with none further coaching!

## System  
You're a time-series evaluation knowledgeable specializing in [domain].  
Your process is to determine patterns, tendencies, and seasonality to forecast precisely.  

## Consumer  
Analyze this time collection: [x1, x2, ..., x96]  
- Dataset: [Weather/Traffic/Sales/etc.]  
- Frequency: [Daily/Hourly/etc.]  
- Options: [List features]  
- Horizon: [Number] intervals forward  

## Activity  
1. Forecast [Number] intervals forward.  
2. Be aware key seasonal or pattern patterns.  

## Constraints  
- Output: Markdown checklist of predictions (2 decimals).  
- Add ≤40-word rationalization of drivers.  

## Analysis Hook  
Finish with: "Confidence: X/10. Assumptions: [...]".

1.3 Neighbor-Augmented Prompting

Typically, one time collection isn’t sufficient. we are able to add “neighbor” collection which can be comparable after which the LLM is in a position spot widespread constructions and enhance predictions:

## System  
You're a time-series analyst with entry to five comparable historic collection.  
Use these neighbors to determine shared patterns and refine predictions.  

## Consumer  
Goal collection: [current time series data]  

Neighbors:  
- Sequence 1: [ ... ]  
- Sequence 2: [ ... ]  
...  

## Activity  
1. Predict the subsequent [h] values of the goal.  
2. Clarify in ≤40 phrases how neighbors influenced the forecast.  

## Constraints  
- Output: Markdown checklist of [h] predictions (2 decimals).  
- Spotlight any divergences from neighbors.  

## Analysis Hook  
Finish with: "Confidence: X/10. Assumptions: [...]".

2. Prompts for Time-Sequence Preprocessing and Evaluation

2.1 Stationarity Testing and Transformation

One of many first issues information scientists should do earlier than modeling time-series information is to test whether or not the collection is stationary.

If it’s not, they should apply transformations like differencing, log, or Field-Cox.

Immediate to Take a look at for Stationary and Apply Transformations

## System  
You're a time-series analyst.  

## Consumer  
Dataset: [N] observations  
- Time interval: [specify]  
- Frequency: [specify]  
- Suspected pattern: [linear / non-linear / seasonal]  
- Enterprise context: [domain]  

## Activity  
1. Clarify find out how to take a look at for stationarity utilizing:  
   - Augmented Dickey-Fuller  
   - KPSS  
   - Visible inspection  
2. If non-stationary, counsel transformations: differencing, log, Field-Cox.  
3. Present Python code (statsmodels + pandas).  

## Constraints  
- Hold rationalization ≤120 phrases.  
- Code needs to be copy-paste prepared.  

## Analysis Hook  
Finish with: "Confidence: X/10. Assumptions: [...]".

2.2 Autocorrelation and Lag Characteristic Evaluation

Autocorrelation in time collection measures how strongly present values are correlated with their very own previous values at totally different lags.

With the precise plots (ACF/PACF), you’ll be able to observe lags that matter most and construct options round them.

Immediate for Autocorrelation

## System  
You're a time-series knowledgeable.    

## Consumer  
Dataset: [brief description]  
- Size: [N] observations  
- Frequency: [daily/hourly/etc.]  
- Uncooked pattern: [first 20–30 values]  

## Activity  
1. Present Python code to generate ACF & PACF plots.  
2. Clarify find out how to interpret:  
   - AR lags  
   - MA parts  
   - Seasonal patterns  
3. Advocate lag options based mostly on vital lags.  
4. Present Python code to engineer these lags (deal with lacking values).  

## Constraints  
- Output: ≤150 phrases rationalization + Python snippets.  
- Use statsmodels + pandas.  

## Analysis Hook  
Finish with: "Confidence: X/10. Key lags flagged: [list]".

2.3 Seasonal Decomposition and Pattern Evaluation

Decomposition helps you see the story behind the info and it helps seeing it in numerous layers: pattern, seasonality, and residuals.

Immediate for Seasonal Decomposition

## System  
You're a time-series knowledgeable.   

## Consumer  
Knowledge: [time series]  
- Suspected seasonality: [daily/weekly/yearly]  
- Enterprise context: [domain]  

## Activity  
1. Apply STL decomposition.  
2. Compute:  
   - Seasonal power Qs = 1 - Var(Residual)/Var(Seasonal+Residual)  
   - Pattern power Qt = 1 - Var(Residual)/Var(Pattern+Residual)  
3. Interpret pattern & seasonality for enterprise insights.  
4. Advocate modeling approaches.  
5. Present Python code for visualization.  

## Constraints  
- Hold rationalization ≤150 phrases.  
- Code ought to use statsmodels + matplotlib.  

## Analysis Hook  
Finish with: "Confidence: X/10. Key enterprise implications: [...]".

3. Anomaly Detection with LLMs

3.1 Direct Prompting for Anomaly Detection

Anomaly detection in time-series is often not a enjoyable process and requires loads of time.

LLMs can act like a vigilant analyst, recognizing outsider values in your information.

Immediate for Anomaly Detection

## System  
You're a senior information scientist specializing in time-series anomaly detection.  

## Consumer  
Context:  
- Area: [Financial/IoT/Healthcare/etc.]  
- Regular working vary: [specify if known]  
- Time interval: [specify]  
- Sampling frequency: [specify]  
- Knowledge: [time series values]  

## Activity  
1. Detect anomalies with timestamps/indices.  
2. Classify as:  
   - Level anomalies  
   - Contextual anomalies  
   - Collective anomalies  
3. Assign confidence scores (1–10).  
4. Clarify reasoning for every detection.  
5. Recommend potential causes (domain-specific).  

## Constraints  
- Output: Markdown desk (columns: Index, Sort, Confidence, Rationalization, Attainable Trigger).  
- Hold narrative ≤150 phrases.  

## Analysis Hook  
Finish with: "Total confidence: X/10. Additional information wanted: [...]".

3.2 Forecasting-Based mostly Anomaly Detection

As a substitute of anomalies straight, one other sensible technique is to forecast what “ought to” occur first, after which measure the place actuality drifts away from these expectations.

These deviations can spotlight anomalies that wouldn’t stand out in one other means.

Right here’s a ready-to-use immediate you’ll be able to strive:

## System  
You're an knowledgeable in forecasting-based anomaly detection.  

## Consumer  
- Historic information: [time series]  
- Forecast horizon: [N periods]  

## Methodology  
1. Forecast the subsequent [N] intervals.  
2. Examine precise vs forecasted values.  
3. Compute residuals (errors).  
4. Flag anomalies the place |precise - forecast| > threshold.  
5. Use z-score & IQR strategies to set thresholds.  

## Activity  
Present:  
- Forecasted values  
- 95% prediction intervals  
- Anomaly flags with severity ranges  
- Advisable threshold values  

## Constraints  
- Output: Markdown desk (columns: Interval, Forecast, Interval, Precise, Residual, Anomaly Flag, Severity).  
- Hold rationalization ≤120 phrases.  

## Analysis Hook  
Finish with: "Confidence: X/10. Threshold methodology used: [z-score/IQR]".

4. Characteristic Engineering for Time-Dependent Knowledge

Good options could make or break your mannequin.

There are simply too many choices: lags to rolling home windows, cyclical options, and exterior variable. There’s rather a lot you’ll be able to add to seize time dependencies.

4.1 Automated Characteristic Creation

The true magic occurs when you engineer significant options that seize tendencies, seasonality, and temporal dynamics. LLMs can truly assist automate this course of by producing a variety of helpful options for you.

Complete Characteristic Engineering Immediate:

## System  
You're a characteristic engineering knowledgeable for time collection.  

## Consumer  
Dataset: 
Half 1: Prompts for Core Methods in Time-Sequence
The put up Prompt Engineering for Time-Series Analysis with Large Language Models appeared first on Towards Data Science.
  
- Goal variable: [specify]  
- Temporal granularity: [hourly/daily/etc.]  
- Enterprise area: [context]  

## Activity  
Create temporal options throughout 5 classes:  
1. **Lag Options**  
   - Easy lags, seasonal lags, cross-variable lags  
2. **Rolling Window Options**  
   - Shifting averages, std/min/max, quantiles  
3. **Time-based Options**  
   - Hour, day, month, quarter, 12 months, DOW, WOY, is_weekend, is_holiday, time since occasions  
4. **Seasonal & Cyclical Options**  
   - Fourier phrases, sine/cosine transforms, interactions  
5. **Change-based Options**  
   - Variations, pct adjustments, volatility measures  

## Constraints  
- Output: Python code utilizing pandas/numpy.  
- Add quick steering on characteristic choice (significance/collinearity).  

## Analysis Hook  
Finish with: "Confidence: X/10. Options most impactful for [domain]: [...]".

4.2 Exterior Variable Integration

It may possibly occur that the goal collection shouldn’t be sufficient to elucidate the complete story.

There are exterior components that always affect our information, like climate, financial indicators, or particular occasions. They’ll add context and enhance forecasts.

The trick is realizing find out how to combine them correctly with out breaking temporal guidelines. Right here’s a immediate to include exogenous variables into your evaluation.

Exogenous Variable Immediate:

## System  
You're a time-series modeling knowledgeable.  
Activity: Combine exterior variables (exogenous options) right into a forecasting pipeline.  

## Consumer  
Major collection: [target variable]  
Exterior variables: [list]  
Knowledge availability: [past only / future known / mixed]  

## Activity  
1. Assess variable relevance (correlation, cross-correlation).  
2. Align frequencies and deal with resampling.  
3. Create interplay options between exterior & goal.  
4. Apply time-aware cross-validation.  
5. Choose options fitted to time-series fashions.  
6. Deal with lacking values in exterior variables.  

## Constraints  
- Output: Python code for  
  - Knowledge alignment & resampling  
  - Cross-correlation evaluation  
  - Characteristic engineering with exterior vars  
  - Mannequin integration:  
    - ARIMA (with exogenous vars)  
    - Prophet (with regressors)  
    - ML fashions (with exterior options)  

## Analysis Hook  
Finish with: "Confidence: X/10. Most impactful exterior variables: [...]".

Closing Ideas

I hope this information has given you a large number to digest and take a look at.

It’s a toolbox filled with researched strategies for utilizing LLMs in time-series evaluation.

Success in time-series information comes once we respect the quirks of temporal information, craft prompts that spotlight these quirks, and validate every little thing with the precise analysis strategies.

Thanks for studying! Keep tuned for Half 2 😉

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I provide mentorship on profession development and transition here.

If you wish to help my work, you’ll be able to buy me my favorite coffee: a cappuccino. 😊

References

LLMs for Predictive Analytics and Time-Series Forecasting

Smarter Time Series Predictions With Less Effort

Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition

LLMs in Time-Series: Transforming Data Analysis in AI

kdd.org/exploration_files/p109-Time_Series_Forecasting_with_LLMs.pdf