Dataset Cleaning Tips
Cleaning Time-Series Data for Reliable Analysis
This prompt helps data science teams clean and preprocess time-series data to improve reliability and accuracy for analysis or machine learning. It focuses on handling missing timestamps, smoothing noise, and ensuring consistent intervals across the dataset.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Engineering
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Review the dataset’s time index for missing or irregular timestamps.
Define the target frequency and purpose of the time-series analysis (e.g., forecasting, anomaly detection).
Identify known patterns or domain knowledge that may guide cleaning decisions.
THE PROMPT
Help create a plan for cleaning and preprocessing time-series data in [specific dataset or domain, e.g., IoT sensor data]. Focus on:
Handling Missing Timestamps: Recommending strategies, such as, ‘Identify gaps in the time index and fill them by interpolation, forward-filling, or adding placeholders for consistency.’
Smoothing Noise: Suggesting methods, like, ‘Apply moving averages, Gaussian filters, or exponential smoothing to reduce noise without distorting trends.’
Ensuring Uniform Intervals: Including validation steps, such as, ‘Detect irregular time intervals and resample the data to a consistent frequency using aggregation or interpolation techniques.’
Outlier Detection: Proposing checks, like, ‘Identify and handle anomalies in the time-series data using statistical thresholds or domain-specific rules.’
Scaling and Transformation: Recommending preprocessing methods, such as, ‘Normalize or detrend the data to prepare it for seasonal decomposition or forecasting models.’
Provide a structured cleaning plan that ensures time-series data is ready for analysis or predictive modeling while preserving its key patterns and trends. If additional details about the dataset or its application are needed, ask clarifying questions to refine the guidance.
Bonus Add-On Prompts
Propose strategies for handling multivariate time-series data with mixed feature types.
Suggest methods for imputing missing data in seasonal or cyclic time-series datasets.
Highlight techniques for visualizing cleaned time-series data to validate preprocessing steps.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on cleaning time-series data for real-time applications like stock market analysis.
Include tips for handling time-zone inconsistencies in globally collected data.
Propose ways to deal with seasonal and trend components during preprocessing.
Highlight tools like Pandas, Prophet, or Darts for time-series cleaning and modeling.
Add suggestions for detecting abrupt changes in the data using change-point analysis.
WHEN TO USE
During the preprocessing phase of time-series datasets for machine learning or analysis.
To standardize time-series data collected from sensors, financial records, or user activity logs.
When preparing datasets for time-series forecasting or anomaly detection.
WHEN NOT TO USE
For non-time-series data without a temporal component.
If the dataset already follows consistent intervals and is free from anomalies.