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Related Experiment Video

Updated: Sep 8, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

Two preprocessing algorithms for climate time series.

Stephan Schlüter¹, Milena Kresoja²

¹Faculty of Mathematics, Natural and Economic Science, Ulm University, Ulm, Germany.

Journal of Applied Statistics

|June 16, 2022

Summary

This study introduces two novel algorithms for climate time series preprocessing. The autoregressive method excels at single outliers, while the wavelet transform is best for multiple consecutive outliers in climate data.

Keywords:

Data preprocessing outliers temperature wavelets

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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

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Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Area of Science:

Environmental science
Data science

Background:

Climate time series data often contain outliers that can affect analysis.
Existing preprocessing methods may not be optimal for all types of outliers.

Purpose of the Study:

To propose and evaluate two novel algorithms for preprocessing climate time series data.
To compare the performance of the proposed algorithms against existing methods.

Main Methods:

Autoregressive cost update mechanism for outlier detection.
Wavelet transform for outlier detection, a pattern recognition technique.
Benchmarking using synthetic datasets and real-world temperature measurements.

Main Results:

The autoregressive method is effective for solitary outliers.
The wavelet-based method is superior for multiple consecutive outliers.
Combined application of both methods provides a robust signal preprocessing tool.

Conclusions:

The proposed algorithms offer effective solutions for climate time series preprocessing.
The choice of algorithm depends on the nature of the outliers (solitary vs. consecutive).
These methods enhance the reliability of climate data analysis.