ShreyasP123/anomaly_detection_metrics_data

Dataset Card: Anomaly Detection Metrics Data

Dataset Summary

This dataset contains system performance metrics collected over time for anomaly detection in time series data. It includes multiple system metrics such as CPU load, memory usage, and other resource utilization statistics, along with timestamps and additional attributes.

Dataset Details

• Size: ~7.3 MB (raw JSON), 345 kB (auto-converted Parquet)
• Rows: 46,669
• Format: JSON
• Libraries: datasets, pandas, croissant
• License: MIT

Features

Feature	Type	Description
metric_name	string	Name of the system metric (e.g., system.cpu.load_average.1m, system.memory.usage)
timestamp	string	Timestamp of the recorded metric in ISO format
value	float64	Recorded value of the metric
attributes	dict	Additional metadata (e.g., device, state, direction)

Usage Example

Load Dataset

from datasets import load_dataset

dataset = load_dataset("ShreyasP123/anomaly_detection_metrics_data")
print(dataset["train"][0])  # View first record

Convert to Pandas DataFrame

import pandas as pd

df = pd.DataFrame(dataset["train"])
print(df.head())

Applications

• Anomaly detection in cloud and edge computing environments
• Predictive maintenance based on system performance
• Cybersecurity monitoring for unusual activity
• Resource optimization in distributed systems

Limitations

• Requires domain expertise for correct anomaly labeling
• May not generalize well to all system configurations without retraining
• Timestamp granularity may impact detection accuracy

Citation

If you use this dataset, please cite:

@dataset{shreyasP123_anomaly_detection_metrics_data,
  author = {ShreyasP123},
  title = {Anomaly Detection Metrics Data},
  year = {2025},
  url = {https://huggingface.co/datasets/ShreyasP123/anomaly_detection_metrics_data}
}

Maintainer

• ShreyasP123

If you are having any queries then feel free to contact me .  :)