579K+ Records Longitudinal Time Series Dataset
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£111,400
About
579K+ Records Longitudinal Time Series Dataset
Description
The 579K+ Records Longitudinal Time Series Dataset is a comprehensive collection of sequential observations recorded over time, enabling the analysis of trends, seasonal patterns, temporal dependencies, and long-term changes across one or more entities. The dataset is designed for AI, machine learning, predictive analytics, forecasting, econometrics, and statistical modeling applications where understanding temporal behavior is critical. Its structured chronological format makes it valuable for developing forecasting models, anomaly detection systems, recommendation engines, and decision-support solutions.
Note: The listed price applies to the specified initial batch of 100,000 records. Pricing for larger batches or the complete dataset library varies depending on the number of records, time span and longitudinal depth, number of variables, metadata availability, annotation requirements, data quality, file formats, licensing terms, and customization needs. Final pricing will be determined based on the specific dataset requirements.
Data Product Features
| Feature | Description |
|---|---|
| Timestamp | Date and/or time associated with each observation. |
| Sequential Records | Chronologically ordered observations collected over multiple time periods. |
| Time Series Variables | One or more numerical or categorical variables measured over time. |
| Target Variable | Dependent variable for forecasting or prediction tasks. |
| Static Attributes | Entity-specific metadata that remains constant across observations. |
| Dynamic Attributes | Features that change over time. |
| Missing Value Indicators | Flags identifying unavailable or incomplete observations. |
| Time Frequency | Daily, weekly, monthly, quarterly, yearly, hourly, or custom intervals. |
Distribution
The dataset is delivered in an organized structure suitable for analytics pipelines and AI workflows.
- Format: CSV, XLSX
Data Volume
- Records: 578K+ Records
- Dataset Size: The dataset size may vary depending on the number of records, time intervals, variables, file formats, metadata availability, annotations, and dataset version.
Usage
This data product is ideal for a variety of AI, analytics, and forecasting applications.
- Time Series Forecasting: Predict future values using historical observations.
- Machine Learning: Train regression, classification, and sequence prediction models.
- Deep Learning: Develop LSTM, GRU, Transformer, and Temporal Fusion Transformer models.
- Anomaly Detection: Detect unusual temporal patterns and system failures.
- Business Intelligence: Analyze long-term trends and operational performance.
- Demand Forecasting: Predict customer demand and inventory requirements.
- Financial Analytics: Model stock prices, economic indicators, and market trends.
- Healthcare Analytics: Monitor patient outcomes and longitudinal health records.
- Predictive Maintenance: Forecast equipment failures using sensor histories.
- IoT Analytics: Analyze streaming sensor and telemetry data.
- Climate & Environmental Research: Study weather, temperature, rainfall, and environmental changes.
- Academic Research: Support econometric, statistical, and longitudinal studies.
Coverage
Geographic Coverage
Global
License
CC BY 4.0 (Creative Commons Attribution 4.0 International)
AI Training Rights
InfoBay.AI ensures that all datasets are sourced, curated, and managed with proper ownership verification, licensing documentation, and data provenance records. We hold the necessary rights to license and sublicense the datasets we provide through formal agreements with our data vendors, which grant us the required permissions for commercial licensing and AI training use cases. To ensure transparency and compliance, we maintain relevant documentation and have previously shared redacted agreements for selected datasets as evidence of our data rights and licensing authority.
Data Dictionary
| Column Name | Data Type | Description | Possible Values / Notes |
|---|---|---|---|
| Entity_ID | String | Identifier for each tracked entity | Customer, device, product, patient, organization, etc. |
| Timestamp | DateTime | Observation timestamp | ISO 8601 format |
| Date | Date | Calendar date | DD-MM-YYYY |
| Time | Time | Time of observation | HH:MM:SS |
| Time_Index | Integer | Sequential observation number | Positive integer |
| Target_Variable | Float/Integer | Primary prediction target | Dataset-specific |
| Feature_1 | Float | Time-varying numerical feature | Continuous values |
| Feature_2 | Integer | Additional temporal feature | Integer values |
| Feature_3 | String | Categorical variable | Dataset-specific categories |
| Static_Attribute | String | Entity-level attribute | Constant per entity |
| Dynamic_Attribute | Float | Variable changing over time | Continuous values |
| Event_Flag | Boolean | Indicates significant events | True / False |
| Missing_Value_Flag | Boolean | Missing observation indicator | True / False |
| Source | String | Data source or origin | Dataset-specific |
Considerations
This dataset is provided for research and educational purposes only. It contains only sample data.
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£111,400
Download Dataset in CSV Format
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