Time Series Forecasting using Machine Learning has great practical applicability in business.
- Demand forecasting
- Price prediction
- Trend detection and trend estimation
- Anomaly detection
Yet, the barrier to entry is still very high.
For that reason, we are really excited to announce we are working hard on the initial release of Universal Forecast Engine.
We are building this product with the following goals in mind:
Forecasting is critical for automating and optimizing operational processes and enabling data-driven decision-making.
- Retail sales
- Medical analysis
- Capacity planning
- Sensor network monitoring
- Financial analysis
- Social activity mining
Why traditional approaches fail
Businesses try to use everything from simple spreadsheets to complex demand/financial planning software to generate forecasts, but high accuracy remains elusive for two reasons.
- Traditional forecasts struggle to incorporate large volumes of historical data, missing important signals from the past that are lost in the noise.
- Traditional forecasts rarely incorporate related but independent data, which can offer important context (such as price levels, holidays/events, stock-outs, and marketing promotions). Without the full history and the broader context, most forecasts fail to predict the future accurately.