📦 SKU Batch Forecaster
Upload SKU data and generate forecasts at scale using univariate models
1️⃣ Upload Data
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Drag & drop your file here
Supports CSV and Excel • SKU ID, Date, Value columns required
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Unique SKUs
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Date Range
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Frequency
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Total Rows
Showing first 10 rows
📊 Large Dataset Loaded
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Large Dataset Mode
This dataset is too large to load into the browser memory.
Use Submit Background Job to process forecasts on the server.
The job will run in the background and you can close this browser tab.
The job will run in the background and you can close this browser tab.
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Dataset
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Unique SKUs
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Total Rows
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Date Range
📋 Data Preview (Sample)
First 100 rows
⚙️ Forecast Configuration
📦 SKUs to Forecast
💡 Background jobs process on the server. You can close this tab and check results later.
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🔍 Dataset Intelligence (optional)
📊 Data Health Check
Click "Run Check" for instant data quality analysis
Detects: frequency, duplicates, outliers, stale SKUs, demand patterns
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SKUs
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Rows
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Periods
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Frequency
Issues & Recommendations
Demand Pattern Distribution
⚠️ Duplicate SKU-Dates
📈 Potential Outliers
⏱️ Stale SKU Breakdown
🤖 AI Deep Analysis (optional)
Get contextual AI recommendations based on your data profile
Takes 10-30 seconds • Uses health check results for context
Analyzing 0 SKUs...
This may take 10-30 seconds
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Total SKUs
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Quality Score
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Forecastable
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Date Range
📊 Overview
📈 Patterns
… Quality
🔗 Related SKUs
🤖 AI Insights
📦 Volume Distribution
📈 Trend Summary
🏗 Top 10 SKUs by Volume
🎯 Demand Classification
🔮 Forecastability
📊 Series Length
⚠️ Problem SKUs
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Identify Related SKUs
Find SKUs with correlated demand patterns and detect lead/lag relationships that can improve forecasting accuracy.
Analyzing cross-correlations...
This may take 30-60 seconds for large datasets
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Related Pairs
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With Lead/Lag
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SKU Clusters
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Avg Correlation
2️⃣ Configure Forecast ▶
📋 Column Mapping
Ctrl+click to select multiple. Creates per-combination forecasts (e.g. SKU | Store)
📦 SKUs to Forecast
📦 Horizon
🤖 Model
🧠 Chronos: Pre-trained neural model - great for complex patterns
⚡ Quick Mode: Uses Holt-Winters with minimal validation for maximum speed
🎼 Ensemble: Runs all available models and blends their forecasts using accuracy-based weights
🏗️ Hierarchy
drag to reorder
Which level to generate forecasts for
Forecast at this level, aggregate up and disaggregate down
Optimal (MinT): Forecasts all levels simultaneously and reconciles using minimum-error weighting. Best accuracy but requires sufficient history per node.
⚙️ Advanced
📊 Data Handling
How to handle missing dates between first and last sale
SKUs with no sales for this many days before dataset end are considered discontinued
Dataset Range: -
Potentially Discontinued: - SKUs
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📊 External Regressors (Covariates)
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What are Covariates?
External factors that influence demand:
• Continuous: Price, temperature, GDP
• Binary: Promotion flag, holiday
• Categorical: Region, channel
Supported Models:
✅ Prophet, LightGBM, ARIMA
❌ ETS, Holt-Winters, Croston
• Continuous: Price, temperature, GDP
• Binary: Promotion flag, holiday
• Categorical: Region, channel
Supported Models:
✅ Prophet, LightGBM, ARIMA
❌ ETS, Holt-Winters, Croston
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No additional columns detected for covariates.Upload data with price, promo, or other factor columns.
📦 Future Values for Forecast Horizon
No future values configured - will use extrapolation
⚠️ Selected model does not support covariates. Choose Prophet, LightGBM, or ARIMA, or use Auto-Select.
🖢 New Product Forecasting
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📦 New Product Mode
Configure attributes for a new product to find similar existing SKUs and generate a forecast based on their demand patterns.
Configure attributes for a new product to find similar existing SKUs and generate a forecast based on their demand patterns.
Load a dataset to see available attributes
🔎 Forecasting...
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Completed
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Total
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Errors
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ETA
Initializing...
📊 Forecast Results
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SKUs Forecasted
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Avg MAPE (excl. intermittent)
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Accuracy
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Time
📋 Table
🏗️ Hierarchy
📊 Models
📈 Charts
🤖 AI Assistant
📋 Demand Planning
| SKU ID | Sparkline | Model | Classification | Covariates | MAPE | RMSE | Trend | Actions |
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Model Distribution
MAPE by Model
Demand Classification
Accuracy Distribution
🏗️ Hierarchical View
No hierarchy detected
Showing top 20 forecasts by accuracy
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Anomaly Detection
Scan for unusual patterns, outliers, and potential issues
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Smart Recommendations
Get AI-powered inventory and ordering suggestions
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Priority SKU Selection
Identify which SKUs need detailed review
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Executive Summary
Get a comprehensive AI analysis of all results
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