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

Datasets:

ShreyasP123
/

anomaly_detection_metrics_data