Mobiusi

ETT (Electric Transformer Temperature) Time Series Dataset

#Time Series Prediction #Anomaly Detection #Trend Analysis #Power Monitoring #Equipment Maintenance #Fault Warning #Industrial Automation
  • 500
  • 1.3G
  • CSV
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

In the industrial manufacturing sector, failures in power equipment can lead to significant economic losses and safety issues. Currently, the industry widely applies simplified threshold warning methods, which have low sensitivity to data anomalies and cannot carry out effective prevention in advance. The ETT time series dataset aims to improve the accuracy of electric transformer temperature prediction, catering to the needs of preventive maintenance. The data is collected through high-precision sensors in real industrial environments, simulating complex power load changes. During the data collection process, a strategy combining automated calibration and manual review is adopted, with multiple rounds of annotation and expert team review to ensure data consistency and accuracy. The annotation team consists of 15 people, including power engineers and data scientists. Data preprocessing includes steps such as time series completion, data cleaning, and normalization, and is finally stored in a structured CSV format to facilitate training data models.

Sample Examples

Technical Specifications

FieldTypeDescription
file_namestringFile name
durationstringDuration
qualitystringResolution
temperature_minfloatThe minimum temperature of the transformer recorded in the file.
temperature_maxfloatThe maximum temperature of the transformer recorded in the file.
temperature_averagefloatThe average temperature of the transformer recorded in the file.
temperature_variancefloatThe variance of the temperature data in the file, indicating the fluctuation of temperature.
peak_to_peakfloatThe peak-to-peak value of the temperature data, indicating the difference between extreme values.
observation_countintThe total number of temperature observation data points recorded in the file.
upward_trend_countintThe number of time periods in the file where the temperature shows an upward trend.
downward_trend_countintThe number of time periods in the file where the temperature shows a downward trend.
max_slopefloatThe maximum rate of temperature change recorded in the file.
time_to_peakfloatThe time taken from the start of recording to reach the peak temperature.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the main application of the ETT time series dataset?
The ETT time series dataset is primarily used for predicting and analyzing the temperature of electric transformers to optimize transformer performance and maintenance in industrial manufacturing.
What are the industrial applications of the ETT dataset?
The ETT dataset is mainly applied in the industrial manufacturing sector, particularly in power and energy management.
How does the ETT time series dataset enhance transformer performance in industrial manufacturing?
The ETT dataset helps enhance transformer performance by providing accurate temperature prediction data, preventing overheating issues and improving the efficiency and safety of industrial equipment.
What are the advantages of the ETT dataset in temperature prediction?
The ETT dataset is known for its high quality and accuracy, providing a reliable data foundation for precise temperature predictions of electric transformers.
Why choose the ETT time series dataset for research?
The ETT time series dataset is chosen for research because it provides crucial data support for transformer temperature management and fault prevention in the power industry.

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Cite this Work

@dataset{Mobiusi2026,
  title={ETT (Electric Transformer Temperature) Time Series Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/a7664eb013237340a3b12b9fc0ea6be2?dataset_scene_cate_type=5},
  urldate={2026-02-04},
  keywords={Transformer Temperature Prediction, Industrial Time Series Dataset, Power Equipment Monitoring},
  version={1.0}
}

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