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Datasets:
InfoBayAI
/
Longitudinal_Time_Series_Dataset

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
time-series
temporal-data
forecasting
prediction
sequence-modeling
sequential-data
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet

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This dataset is a large-scale collection of 470,295 longitudinal time-series records, designed to support the development and training of advanced time-series AI systems, sequence modeling, forecasting, and real-world temporal data analytics applications.

It consists of structured sequential data captured over multiple time steps, reflecting dynamic changes, evolving patterns, and temporal dependencies within real-world systems. The dataset is suitable for modeling progression, trend behavior, and time-dependent relationships across multiple variables.

This dataset is highly valuable for building scalable and production-ready AI systems for forecasting, anomaly detection, sequence prediction, behavioral modeling, and decision intelligence applications. Additionally, it can be used in pipelines for Supervised Fine-Tuning (SFT) and advanced time-series machine learning workflows.

Dataset Specification

  • Records: 470,295
  • Format: CSV
  • Domain: Time-Series / Sequential Data
  • Data Type: Structured temporal records
  • Nature: Real-world sequential observations
  • Structure: Multi-step time-dependent data

Key Use Cases

  • Time-series forecasting and prediction
  • Sequential pattern recognition
  • Anomaly detection in temporal data
  • Trend analysis and estimation
  • Behavior modeling over time
  • Dynamic system modeling
  • Event sequence prediction
  • Multivariate time-series analysis
  • AI-driven decision systems
  • Predictive analytics pipelines

Value of This Dataset

  • Enables development of advanced time-series AI models
  • Improves forecasting and trend prediction accuracy
  • Supports real-world sequential learning systems
  • Useful for large-scale temporal pattern analysis
  • Enhances robustness of machine learning pipelines
  • Applicable across finance, IoT, logistics, and general analytics systems

Basic JSON Schema

{
  "Patient Info": "string",
  "Weight (kg) & Date": "string",
  "First & Last Encounter": "string",
  "Visit Dates": "string",
  "HIV Viral Load Date": "string",
  "Viral Load Value (copies/mL)": "string",
  "Baseline CD4": "string",
  "CD4 Count": "string",
  "TB LAM Results & Date": "string",
  "ART Start": "string",
  "ARV Regimen": "string",
  "ARV Regimen Days Dispensed": "string",
  "Death Status": "string",
  "WHO HIV Clinical Stage": "string",
  "DSDM & DSDM Date": "string",
  "TPT Start Date": "string",
  "TPT Status": "string",
  "OVC Screening & Date": "string",
  "OVC Assessment & Date": "string",
  "Pregnancy Status": "string",
  "PMTCT Status": "string",
  "Tuberculosis Status": "string",
  "Baseline Regimen": "string"
}

Data Creation

Procured through formal agreements and generated in the ordinary course of business.

Considerations

This dataset is provided for research and educational purposes only. It contains only sample data. For access to the full dataset and enterprise licensing options, please visit our website InfoBay.AI or contact us directly.
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