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Synthetic Tech and Finance Forecasting Data
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About
This data simulates Global Trend Analysis for 2024, focusing on key metrics across technology, finance, sustainability, and fitness sectors. The data is entirely synthetic and was designed specifically for forecasting exercises and advanced analytical techniques. It provides crucial information such as weekly popularity scores, sentiment scores, growth rates, and geographical trend origins. This product is ideal for developing time-series models and conducting trend clustering studies.
Columns
The dataset includes 10 distinct columns:
- trend_id: A unique identifier assigned to each trend observation.
- trend_name: The specific name of the trend, such as "AI Agents" or "ESG Investing."
- category: The sector the trend belongs to, mainly Tech, Finance, or Sustainability.
- date: The specific date of the record, formatted as YYYY-MM-DD.
- weekly_interest: A normalised score representing the popularity of the trend, ranging from 0 to 100.
- region: The geographic origin of the trend, including examples such as EU and India.
- sentiment: The social media sentiment score related to the trend, ranging from -1 (highly negative) to +1 (highly positive).
- related_brands: A list of top brands associated with the trend (e.g., BlackRock, OpenAI).
- growth_rate: The calculated month-over-month change in interest, expressed as a percentage.
- forecast_2025: The predicted interest score for the year 2025 (0-100).
Distribution
The dataset is typically provided in a CSV format and is named
kaggle_trends_2024.csv, with a file size of approximately 747.97 kB. It contains exactly 10,000 records. There are 10 columns in total, and all fields are validated as 100% complete, meaning there are no missing or mismatched values.Usage
The data is excellently suited for:
- Time-series forecasting: Predicting future movements in simulated market trends.
- Sentiment analysis: Studying how simulated social media sentiment correlates with growth rates.
- Trend clustering: Grouping similar trends based on metrics like weekly interest and category.
- Simulating scenario analysis: Testing forecasting models on synthetic business data, particularly in the AI and sustainability fields.
Coverage
Time Range: The records span a period from 1 January 2023 up to 29 April 2025.
Geographic Scope: The data incorporates regional information, with notable coverage for areas such as the EU and India.
Sector Scope: Focus areas include technology, finance, and sustainability, with specific trends relating to AI Agents and ESG Investing.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For testing and developing new machine learning models for time-series prediction.
- Financial Analysts: To practise forecasting techniques on simulated financial trends.
- Academic Researchers: For studies focused on market trend dynamics and sentiment correlation using non-real-world data.
Dataset Name Suggestions
- Simulated Global Trend Analysis 2024
- Synthetic Tech and Finance Forecasting Data
- Global Sentiment and Growth Rates Simulation
- Time Series Trend Data 2023-2025
Attributes
Original Data Source:Synthetic Tech and Finance Forecasting Data
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