Target Audience

Small to medium-sized businesses (SMBs) with historical time-stamped data (e.g., sales, inventory) stored in Excel or similar tools.

Challenge

Businesses struggle to extract actionable insights from existing time-stamped data (e.g., sales trends, material costs) to predict future demand, inventory needs, or operational risks without requiring deep machine learning expertise.

Solution Approach

Use pre-trained foundation models (e.g., Nixtla TimeGPT-1, Amazon Chronos) to analyze historical data patterns and generate forecasts directly from Excel or other data sources, eliminating the need for custom model training.

Value Add

Enables SMBs to quickly derive predictive insights from existing data, reducing uncertainty in decision-making (e.g., demand planning, cost optimization) with minimal technical effort.

References

Various foundational models, tools like TimeGPT offer Excel plugins for seamless integration.

Read more here: https://www.infoworld.com/article/3543468/5-ways-companies-can-use-time-series-forecasting.html