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# Inventory Forecasting

Vintaflow projects future demand for each product inventory using a statistical forecasting model. This article explains how to choose a model, tune it, and read the accuracy score.

## Where to Find Forecast Settings

1. Navigate to **Inventory** and select a warehouse
2. Open the product inventory you want to configure
3. Click **Forecast Settings**

The dialog is titled **Forecast Settings** and configures the model and hyperparameters for that inventory record.

## Configuration Mode

Choose how this inventory gets its model:

| Mode | Behaviour |
|------|-----------|
| **Preset Model** | Pick an algorithm and configure hyperparameters yourself, or let the trainer auto-tune them |
| **Inherit from Host** | Mirror the forecast configuration of another product inventory in your account |

Use **Inherit from Host** when several products behave the same way - configure one well, then point the others at it.

## Available Algorithms

| Algorithm | Best For |
|-----------|----------|
| **None** | No algorithm applied. Bring your own forecast, or receive zeros by default |
| **Intermittent** | Sporadic, lumpy demand with frequent zero periods |
| **Simple Moving Average** | Stable, low-volatility products. Averages demand over a fixed lookback window |
| **Holt-Winters** | Exponential smoothing with additive trend and seasonality. Robust for stable seasonal products |
| **Prophet** | Meta's time-series model. Handles missing data, outliers, and multiple seasonality patterns automatically |
| **SARIMA** | Seasonal ARIMA. Adds seasonal differencing and lag terms for periodic patterns |
| **STLF** | STL decomposition followed by a Theta forecast on the seasonally adjusted series. Good for strongly seasonal products |
| **Heuristic** | Simple statistical baseline using mean, median, or last value |

|| Choose **Intermittent** when more than 50% of your historical demand observations are zero. Continuous-demand models produce systematically biased forecasts on that kind of data.

Selecting an algorithm shows its description inline, so you can confirm the choice before saving.

## Reading the Accuracy Score

When a model has already been trained, a banner at the top of the dialog shows the current algorithm and its **SMAPE score** (Symmetric Mean Absolute Percentage Error). Lower is better.

| Score | Colour | Interpretation |
|-------|--------|----------------|
| 0.000 - 0.500 | Green | Good fit |
| 0.501 - 1.000 | Amber | Acceptable, worth reviewing |
| Above 1.000 | Red | Poor fit - try a different algorithm |

||| Selecting a new algorithm replaces the currently trained model and its score. The new model has to be trained before a fresh score appears.

## Hyperparameters

Algorithms that accept hyperparameters show a **Hyperparameters** section once selected. Defaults are filled in for you:

* **Holt-Winters** - additive trend, additive seasonality, 52 seasonal periods
* **Prophet** - additive seasonality mode, yearly seasonality 10, changepoint prior scale 0.05, seasonality prior scale 10.0, changepoint range 0.8
* **SARIMA** - p 1, d 1, q 1, no trend, seasonal order (1, 0, 1, 52)
* **STLF** - Theta method, m 52
* **Heuristic** - mean

The value `52` reflects weekly data over a yearly cycle. Change it only if your demand cycle is genuinely a different length.

## Skip Trainer

Tick **Skip Trainer** to disable auto-hyperparameterization and performance index recalculation.

| Leave Skip Trainer off unless you have deliberately hand-tuned your hyperparameters. With it off, Vintaflow tunes the model for you and keeps the accuracy score current.

## Saving

Click **Save** to apply the configuration. The forecast is recalculated on the next run and the results appear in the inventory forecast chart and weekly table. See [Inventory Management](https://help.vintaflow.com/en/article/inventory-management-eknnwq/).

## Tips

* Start with the default algorithm and only change it if the SMAPE score is poor
* Seasonal products need at least one full cycle of history before a seasonal model is meaningful
* Products with sparse or brand-new history do better on **Heuristic** or **Intermittent** than on Prophet or SARIMA
* Use **Inherit from Host** to keep families of similar products consistent