LLM-Powered Time-Series Analysis | Towards Data Science

knowledge at all times brings its personal set of puzzles. Each knowledge scientist finally hits that wall the place conventional strategies begin to really feel… limiting.

However what for those who may push past these limits by constructing, tuning, and validating superior forecasting fashions utilizing simply the proper immediate?

Massive Language Fashions (LLMs) are altering the sport for time-series modeling. If you mix them with good, structured immediate engineering, they may also help you discover approaches most analysts haven’t thought-about but.

They’ll information you thru ARIMA setup, Prophet tuning, and even deep studying architectures like LSTMs and transformers.

This information is about superior immediate methods for mannequin growth, validation, and interpretation. On the finish, you’ll have a sensible set of prompts that will help you construct, evaluate, and fine-tune fashions sooner and with extra confidence.

All the pieces right here is grounded in analysis and real-world instance, so that you’ll go away with ready-to-use instruments.

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

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

On this article:

1. Superior Mannequin Growth Prompts
2. Prompts for Mannequin Validation and Interpretation
3. Actual-World Implementation Instance
4. Greatest Practices and Superior Ideas
5. Immediate Engineering cheat sheet!

1. Superior Mannequin Growth Prompts

Let’s begin with the heavy hitters. As you may know, ARIMA and Prophet are nonetheless nice for structured and interpretable workflows, whereas LSTMs and transformers excel for complicated, nonlinear dynamics.

One of the best half? With the fitting prompts you save quite a lot of time, for the reason that LLMs grow to be your private assistant that may arrange, tune, and verify each step with out getting misplaced.

1.1 ARIMA Mannequin Choice and Validation

Earlier than we go forward, let’s make sure that the classical baseline is stable. Use the immediate under to determine the fitting ARIMA construction, validate assumptions, and lock in a reliable forecast pipeline you possibly can evaluate every thing else in opposition to.

Complete ARIMA Modeling Immediate:

"You might be an skilled time collection modeler. Assist me construct and validate an ARIMA mannequin:

Dataset: 
Half 2: Prompts for Superior Mannequin Growth
The publish LLM-Powered Time-Series Analysis appeared first on Towards Data Science.

Knowledge: [sample of time series]

Section 1 - Mannequin Identification:
1. Check for stationarity (ADF, KPSS checks)
2. Apply differencing if wanted
3. Plot ACF/PACF to find out preliminary (p,d,q) parameters
4. Use info standards (AIC, BIC) for mannequin choice

Section 2 - Mannequin Estimation:
1. Match ARIMA(p,d,q) mannequin
2. Verify parameter significance
3. Validate mannequin assumptions:
   - Residual evaluation (white noise, normality)
   - Ljung-Field check for autocorrelation
   - Jarque-Bera check for normality

Section 3 - Forecasting & Analysis:
1. Generate forecasts with confidence intervals
2. Calculate forecast accuracy metrics (MAE, MAPE, RMSE)
3. Carry out walk-forward validation

Present full Python code with explanations."

1.2 Prophet Mannequin Configuration

Obtained recognized holidays, clear seasonal rhythms, or changepoints you’d prefer to “deal with gracefully”? Prophet is your good friend.

The immediate under frames the enterprise context, tunes seasonalities, and builds a cross-validated setup so you possibly can belief the outputs in manufacturing.

Prophet Mannequin Setup Immediate:

"As a Fb Prophet skilled, assist me configure and tune a Prophet mannequin:

Enterprise context: [specify domain]
Knowledge traits:
- Frequency: [daily/weekly/etc.]
- Historic interval: [time range]
- Recognized seasonalities: [daily/weekly/yearly]
- Vacation results: [relevant holidays]
- Development modifications: [known changepoints]

Configuration duties:
1. Knowledge preprocessing for Prophet format
2. Seasonality configuration:
   - Yearly, weekly, each day seasonality settings
   - Customized seasonal parts if wanted
3. Vacation modeling for [country/region]
4. Changepoint detection and prior settings
5. Uncertainty interval configuration
6. Cross-validation setup for hyperparameter tuning

Pattern knowledge: [provide time series]

Present Prophet mannequin code with parameter explanations and validation strategy."

1.3 LSTM and Deep Studying Mannequin Steering

When your collection is messy, nonlinear, or multivariate with long-range interactions, it’s time to degree up.

Use the LSTM immediate under to craft an end-to-end deep studying pipeline since preprocessing to coaching tips that may scale from proof-of-concept to manufacturing.

LSTM Structure Design Immediate:

"You're a deep studying skilled specializing in time collection. Design an LSTM structure for my forecasting downside:

Drawback specs:
- Enter sequence size: [lookback window]
- Forecast horizon: [prediction steps]
- Options: [number and types]
- Dataset measurement: [training samples]
- Computational constraints: [if any]

Structure issues:
1. Variety of LSTM layers and items per layer
2. Dropout and regularization methods
3. Enter/output shapes for multivariate collection
4. Activation capabilities and optimization
5. Loss perform choice
6. Early stopping and studying fee scheduling

Present:
- TensorFlow/Keras implementation
- Knowledge preprocessing pipeline
- Coaching loop with validation
- Analysis metrics calculation
- Hyperparameter tuning recommendations"

2. Mannequin Validation and Interpretation

You understand that nice fashions are each correct, dependable and explainable.

This part helps you stress-test efficiency over time and unpack what the mannequin is absolutely studying. Begin with strong cross-validation, then dig into diagnostics so you possibly can belief the story behind the numbers.

2.1 Time-Collection Cross-Validation

Stroll-Ahead Validation Immediate:

"Design a strong validation technique for my time collection mannequin:

Mannequin sort: [ARIMA/Prophet/ML/Deep Learning]
Dataset: [size and time span]
Forecast horizon: [short/medium/long term]
Enterprise necessities: [update frequency, lead time needs]

Validation strategy:
1. Time collection break up (no random shuffling)
2. Increasing window vs sliding window evaluation
3. A number of forecast origins testing
4. Seasonal validation issues
5. Efficiency metrics choice:
   - Scale-dependent: MAE, MSE, RMSE
   - Proportion errors: MAPE, sMAPE  
   - Scaled errors: MASE
   - Distributional accuracy: CRPS

Present Python implementation for:
- Cross-validation splitters
- Metrics calculation capabilities
- Efficiency comparability throughout validation folds
- Statistical significance testing for mannequin comparability"

2.2 Mannequin Interpretation and Diagnostics

Are residuals clear? Are intervals calibrated? Which options matter? The immediate under provides you a radical diagnostic path so your mannequin is accountable.

Complete Mannequin Diagnostics Immediate:

"Carry out thorough diagnostics for my time collection mannequin:

Mannequin: [specify type and parameters]
Predictions: [forecast results]
Residuals: [model residuals]

Diagnostic checks:
1. Residual Evaluation:
   - Autocorrelation of residuals (Ljung-Field check)
   - Normality checks (Shapiro-Wilk, Jarque-Bera)
   - Heteroscedasticity checks
   - Independence assumption validation

2. Mannequin Adequacy:
   - In-sample vs out-of-sample efficiency
   - Forecast bias evaluation
   - Prediction interval protection
   - Seasonal sample seize evaluation

3. Enterprise Validation:
   - Financial significance of forecasts
   - Directional accuracy
   - Peak/trough prediction functionality
   - Development change detection

4. Interpretability:
   - Function significance (for ML fashions)
   - Part evaluation (for decomposition fashions)
   - Consideration weights (for transformer fashions)

Present diagnostic code and interpretation tips."

3. Actual-World Implementation Instance

So, we’ve explored how prompts can information your modeling workflow, however how will you really use them?

I’ll present you now a fast and reproducible instance displaying how one can really use one of many prompts inside your personal pocket book proper after coaching a time-series mannequin.

The concept is straightforward: we are going to make use of considered one of prompts from this text (the Stroll-Ahead Validation Immediate), ship it to the OpenAI API, and let an LLM give suggestions or code recommendations proper in your evaluation workflow.

Step 1: Create a small helper perform to ship prompts to the API

This perform, ask_llm(), connects to OpenAI’s Responses API utilizing your API key and sends the content material of the immediate.

Don’t forget yourOPENAI_API_KEY ! You need to put it aside in your surroundings variables earlier than operating this.

After that, you possibly can drop any of the article’s prompts and get recommendation and even code that is able to run.

# %pip -q set up openai  # Provided that you do not have already got the SDK

import os
from openai import OpenAI


def ask_llm(prompt_text, mannequin="gpt-4.1-mini"):
    """
    Sends a single-user-message immediate to the Responses API and returns textual content.
    Swap 'mannequin' to any out there textual content mannequin in your account.
    """
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        print("Set OPENAI_API_KEY to allow LLM calls. Skipping.")
        return None

    shopper = OpenAI(api_key=api_key)
    resp = shopper.responses.create(
        mannequin=mannequin,
        enter=[{"role": "user", "content": prompt_text}]
    )
    return getattr(resp, "output_text", None)

Let’s assume your mannequin is already educated, so you possibly can describe your setup in plain English and ship it by way of the immediate template.

On this case, we’ll use the Stroll-Ahead Validation Immediate to have the LLM generate a strong validation strategy and associated code concepts for you.

walk_forward_prompt = f"""
Design a strong validation technique for my time collection mannequin:

Mannequin sort: ARIMA/Prophet/ML/Deep Studying (we used SARIMAX with exogenous regressors)
Dataset: Every day artificial retail gross sales; 730 rows from 2022-01-01 to 2024-12-31
Forecast horizon: 14 days
Enterprise necessities: short-term accuracy, weekly replace cadence

Validation strategy:
1. Time collection break up (no random shuffling)
2. Increasing window vs sliding window evaluation
3. A number of forecast origins testing
4. Seasonal validation issues
5. Efficiency metrics choice:
   - Scale-dependent: MAE, MSE, RMSE
   - Proportion errors: MAPE, sMAPE
   - Scaled errors: MASE
   - Distributional accuracy: CRPS

Present Python implementation for:
- Cross-validation splitters
- Metrics calculation capabilities
- Efficiency comparability throughout validation folds
- Statistical significance testing for mannequin comparability
"""

wf_advice = ask_llm(walk_forward_prompt)
print(wf_advice or "(LLM name skipped)")

When you run this cell, the LLM’s response will seem proper in your pocket book, normally as a brief information or code snippet you possibly can copy, adapt, and check.

It’s a easy workflow, however surprisingly highly effective: as a substitute of context-switching between documentation and experimentation, you’re looping the mannequin straight into your pocket book.

You’ll be able to repeat this similar sample with any of the prompts from earlier, for instance, swap within the Complete Mannequin Diagnostics Immediate to have the LLM interpret your residuals or counsel enhancements to your forecast.

4. Greatest Practices and Superior Ideas

4.1 Immediate Optimization Methods

Iterative Immediate Refinement:

1. Begin with primary prompts and regularly add complexity, don’t attempt to do it excellent at first.
2. Check completely different immediate constructions (role-playing vs. direct instruction, and so forth)
3. Validate how efficient the prompts are with completely different datasets
4. Use few-shot studying with related examples
5. Add area information and enterprise context, at all times!

Relating to token effectivity (if prices are a priority):

• Attempt to hold a stability between info completeness and token utilization
• Use patch-based approaches to cut back enter measurement​
• Implement prompt caching for repeated patterns
• Consider with your team trade-offs between accuracy and computational cost

 👉 Get the full prompt cheat sheet when you subscribe to Sara’s AI Automation Digest — serving to tech professionals automate actual work with AI, each week. You’ll additionally get entry to an AI device library.

I supply mentorship on profession development and transition here.

If you wish to help my work, you possibly can buy me my favorite coffee: a cappuccino. 

References

MingyuJ666/Time-Series-Forecasting-with-LLMs: [KDD Explore’24]Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities

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