Time Series Forecasting with AI

Time series forecasting predicts future values based on historical patterns. AI models achieve 85-95% accuracy vs 60-70% traditional methods, enabling better demand planning, inventory optimization, and resource allocation.

Key Algorithms

1. LSTM (Long Short-Term Memory)

• Deep learning for complex patterns
• Captures long-term dependencies
• Best for: sales, stock prices, energy demand
• 85-92% accuracy on multi-step forecasts

2. Prophet (Facebook)

• Additive model with trend, seasonality, holidays
• Easy to use, interpretable
• Automatic detection of changepoints
• Best for: business metrics with strong seasonality

3. XGBoost/LightGBM

• Gradient boosting with feature engineering
• Fast training, handles missing data
• Best for: structured time series with many features
• Wins many Kaggle competitions

4. ARIMA/SARIMA

• Classical statistical methods
• Good baseline, interpretable
• Best for: stationary series, simple patterns

Applications

Demand Forecasting

• Retail: SKU-level demand prediction (85-95% accuracy)
• Manufacturing: Production planning and raw material ordering
• E-commerce: Inventory optimization across warehouses
• 30-50% reduction in stockouts and overstock

Sales Forecasting

• Revenue prediction for budgeting and planning
• Territory and rep-level forecasts
• New product launch predictions
• Promotional impact analysis

Energy & Utilities

• Electricity load forecasting (90-95% accuracy)
• Solar/wind power generation prediction
• Peak demand management
• 15-25% reduction in energy costs

Anomaly Detection

• Equipment failure prediction
• Fraud detection in transactions
• Network intrusion detection
• Quality control in manufacturing

Feature Engineering

• Lag Features: Previous values (t-1, t-7, t-30)
• Rolling Statistics: Moving averages, std dev
• Seasonal Indicators: Day of week, month, holidays
• External Variables: Weather, events, promotions
• Trend: Linear, polynomial, piecewise

Implementation Stack

Libraries: Prophet, statsmodels, TensorFlow/PyTorch (LSTM), XGBoost, LightGBM

Tools: Pandas, NumPy, Scikit-learn for preprocessing

Deployment: FastAPI, Docker, scheduled retraining

Best Practices

1. Data Quality: Handle missing values, outliers
2. Train/Test Split: Chronological split (no data leakage)
3. Cross-Validation: Time series CV (rolling window)
4. Multiple Models: Ensemble for robustness
5. Monitoring: Track forecast accuracy over time
6. Regular Retraining: Monthly or when drift detected

Case Study: E-commerce Demand Forecasting

• Scale: 5K SKUs, 50 stores, 2 years history
• Model: Ensemble (LSTM + XGBoost + Prophet)
• Results:
  • Forecast accuracy: 91% (within 10% of actual)
  • Stockouts: -42%
  • Excess inventory: -38%
  • Working capital: -₹4.2Cr (inventory reduction)
  • Sales increase: +12% (better availability)

Pricing

• Single Product/Metric: ₹8-15L
• Multi-SKU System: ₹20-50L
• Enterprise Platform: ₹60L-2Cr
• Timeline: 6-12 weeks