Forecast product prices 30, 60, and 90 days into the future using advanced time-series machine learning
Uses AR(1) autoregressive models with trend and seasonal decomposition
See upper/lower bounds at 80% confidence level for more realistic expectations
Factored in Australian retail calendar (Boxing Day, Black Friday, back-to-school)
System collects and analyzes historical price data for your product across all retailers
Price data is split into trend (overall direction), seasonal (recurring patterns), and residual components
Autoregressive AR(1) model is fitted to identify price momentum and dependencies
30, 60, and 90-day predictions generated with confidence intervals showing uncertainty
Australian retail calendar patterns (sales, holidays) are applied to adjust predictions
Demo Mode: Below is a sample prediction for a Samsung 65" QLED TV. The model shows how prices typically decline post-holiday and during clearance seasons.
Interactive chart showing historical prices and predicted trend with confidence intervals (shaded area).
See expected price changes for upcoming months based on AU retail calendar patterns.
Prices are decomposed into three components: trend (estimated via moving average), seasonal (monthly patterns), and residual (random noise). This allows the model to understand both long-term direction and recurring patterns.
An AR(1) model captures price momentum: tomorrow's price depends on today's price plus an error term. The slope coefficient indicates whether prices tend to move up or down over time.
Predictions are provided with 80% confidence intervals. These grow wider further into the future (uncertainty increases), giving you a realistic view of forecast reliability. The interval shows where you should expect the actual price to land with 80% probability.
The model is tuned to Australian retail patterns:
The card displays R² (coefficient of determination) showing how well the model fits historical data. Values above 0.7 indicate good fit; 0.3-0.5 suggests high volatility; below 0.3 indicates unpredictable pricing.