| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - time-series |
| - temporal-data |
| - forecasting |
| - prediction |
| - sequence-modeling |
| - sequential-data |
| - machine-learning |
| - artificial-intelligence |
| - deep-learning |
| - analytics |
| - trend-analysis |
| - anomaly-detection |
| - predictive-modeling |
| - multivariate-time-series |
| - event-data |
| - tabular-data |
| - csv |
| - llm |
| - sft |
| - rlhf |
| - self-supervised-learning |
| - decision-intelligence |
| - data-science |
| - forecasting-models |
| - sequence-prediction |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| pretty_name: Time Series Dataset |
| --- |
| |
| **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.** |
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| 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. |
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| 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. |
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| --- |
|
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| # 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 |
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| --- |
|
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| # Key Use Cases |
|
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| * 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 |
|
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| --- |
|
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| # Value of This Dataset |
|
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| * 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 |
|
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| --- |
| **Basic JSON Schema** |
|
|
| ```json |
| { |
| "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 |
|
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| Procured through formal agreements and generated in the ordinary course of business. |
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| --- |
|
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|
|
| # Considerations |
|
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| 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](https://infobay.ai) or contact us directly. |
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|
| * Ph: (+91) 8303174762 |
| * Email: datareq@infobay.ai |
