GRIM Forecast Studio
Inventory Demand Forecasting — Train, Test, Deploy, Monitor
Total Series
-
SKU-Location combinations
Forecast Horizon
24w
Weekly predictions ahead
Algorithms
12
Competing models
Last Selection
-
Model selection cycle
Predictions
-
Total forecast rows
Forecast Overview
Series Classification Distribution
Model Assignment Distribution
Pipeline Architecture
1
Ingest
DuckDB
2
Build
Time Series
3
Classify
4-Tier
4
Select
MAPE Arena
5
Forecast
8 Algorithms
6
Output
Post-Process
7
Monitor
Error Track
Series Explorer
Run a forecast first
Click "Run Forecast" to generate predictions
Select a Series
Click a series from the list to view its forecast
Model Arena — Algorithm Competition
Head-to-Head Win Rate Matrix
Series Classification
| Series | Class | Zero% | Weeks | Model |
|---|
Forecast Configuration
Forecast Parameters
Horizon24 weeks
Selection Frequency30 days
CPU Cores23
Classification Thresholds
Dead Lookback180d
Intermittent Threshold0.6
Dead Threshold0.95
LightGBM Hyperparameters
Estimators300
Learning Rate0.05
StrategyRecursive
Algorithm Registry
LightGBMON
XGBoostON
AutoARIMAON
AutoETSON
AutoCESON
AutoThetaON
NaiveON
IMAPAON
CrostonON
SBAON
TSBON
QuantileON
Error Monitoring & Degradation
Degradation Threshold
20%
MAPE increase triggers flag
Lookback Window
4w
Recent evaluation window
Log Retention
12w
Prediction history kept
Monitoring Pipeline
1. Log Predictions
Save each cycle's forecasts with dates
2. Compute Recent MAPE
Match predictions to actuals over last 4 weeks
3. Flag Degraded Series
Compare to baseline — flag if MAPE grew >20%
4. Priority Re-Selection
Flagged series get priority in next model selection
Train & Deploy
Quick Forecast Run
Run forecasts using cached model assignments (fast). Use when model selection is up to date.
Full Re-Train & Deploy
Force full model selection competition (MAPE arena), then forecast. Slower but ensures best models are selected.
Pipeline Run Log
Ready to run. Click a button above to start the forecast pipeline.
Algorithm Feature Summary
| Algorithm | Type | Series Class | Speed | Strengths |
|---|---|---|---|---|
| LightGBM | ML / Tree | ACTIVE | Fast | General demand, sparse data, interpretable |
| AutoARIMA | Statistical | ACTIVE | Medium | Stationary, mildly seasonal series |
| AutoETS | State-space | ACTIVE | Medium | Clear trend + seasonality |
| AutoCES | Complex Exp. | ACTIVE | Medium | Complex seasonal patterns |
| AutoTheta | Decomposition | ACTIVE | Fast | M3/M4 competition winner |
| Naive | Baseline | ALL | Instant | Universal fallback, no-pattern safety net |
| IMAPA | Intermittent | INTERMITTENT | Medium | Lumpy/sparse demand (>60% zeros) |
| Quantile | ML / Quantile | ACTIVE | Fast | P10/P50/P90 prediction intervals |