Categories: AI/ML Research

7 Pandas Tricks for Time-Series Feature Engineering

Feature engineering is one of the most important steps when it comes to building effective machine learning models, and this is no less important when dealing with time-series data.

Time-Series Transformation Toolkit: Feature Engineering for Predictive Analytics

In time series analysis and forecasting , transforming data is often necessary to uncover underlying patterns, stabilize properties like variance, and improve the performance of predictive models.

August 7, 2025

In "AI/ML Research"

Can LLM Embeddings Improve Time Series Forecasting? A Practical Feature Engineering Approach

Using large language models (LLMs) — or their outputs, for that matter — for all kinds of machine learning-driven tasks, including predictive ones that were already being solved long before language models emerged, has become something of a trend.

February 28, 2026

In "AI/ML Research"

The Concise Guide to Feature Engineering for Better Model Performance

Feature engineering helps make models work better. It involves selecting and modifying data to improve predictions. This article explains feature engineering and how to use it to get better results. What is Feature Engineering? Raw data is often messy and not ready for predictions. Features are important details in your…

September 19, 2024

In "AI/ML Research"

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