• views: 74
• date: 17 July 2026
• posted by: voska89

Forecasting time-based data is a common challenge across industries, but many models fall short because they fail to capture the underlying structure of time itself. Without the right preparation and feature engineering, important signals like trends, seasonality, and temporal relationships are lost.
In this course, Feature Engineering for Time Series Modeling, you'll gain the ability to transform raw time series data into datasets that drive more accurate forecasts.
First, you'll explore how to properly prepare time series data, including handling missing values, aligning timestamps, and reducing noise.
Next, you'll discover how to engineer time-aware features such as lag variables, rolling statistics, and calendar-based signals that capture temporal relationships.
Finally, you'll learn how to apply transformations and modeling strategies that improve forecasting performance while balancing accuracy, interpretability, and operational complexity.
When you're finished with this course, you'll have the skills and knowledge of feature engineering needed to build more accurate and reliable forecasting models.

Homepage

https://app.pluralsight.com/ilx/video-courses/feature-engineering-time-series-modeling/course-overview

   
   
   

Forex Trading

Crypto Trading. 20 USD Bonus after KYC

We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm

0 1 2 3 4 5

| Print version |

	Information
		Users of GUESTS are not allowed to comment this publication.