Sanvia
4 State-of-the-Art Models

Forecasting Models Built for Real Business Needs

From statistical classics to cutting-edge machine learning. Each model optimized on real-world data and designed for specific forecasting scenarios.

4
Models
20+
Features
Auto
Optimization
Multi
Column
Need something more specialized? We build custom models

Purpose-Built for Time Series Forecasting

Why these models outperform generic approaches

Unlike basic statistical models (simple moving averages, linear regression) or general-purpose large language models, our forecasting suite is specifically designed and optimized for time series prediction. Each model incorporates sophisticated techniques for handling temporal dependencies, seasonality, and trend changes that simpler approaches miss entirely.

Fine-Tuned on Real Business Data

Model hyperparameters have been optimized through extensive testing on hundreds of real-world sales datasets across different industries, seasonalities, and business cycles.

Parallel Processing

All models can run simultaneously, allowing you to compare results and select the best performer for your specific data patterns.

Multi-column forecasting is a key differentiator. You can forecast multiple related time series in a single workflow—whether that's different product lines, sales across geographical territories (e.g., North America, EMEA, APAC), or various business metrics.

Why not LLMs? While large language models excel at text generation and pattern recognition in language, they are not designed for numerical time series forecasting. They lack the mathematical foundations for handling temporal autocorrelation, seasonal decomposition, and stationary vs. non-stationary data.

Detailed Model Information

LightGBM Ensemble

Advanced gradient boosting with intelligent feature engineering

Advanced Complexity30+ Data Points✓ External Features

Overview

State-of-the-art gradient boosting that automatically engineers relevant features from your data. Perfect for capturing intricate patterns and non-linear relationships.

Best For:

Complex patterns with external features

Technical Specifications

Min Data Points:
30
Optimal Range:
50-500
External Features:
✓ Yes
Multi-Column:
✓ Yes
Confidence Intervals:
✓ Yes

Key Features

  • Auto-detected optimal lag features (1-12 periods)
  • Fourier features for multiple seasonal patterns
  • Rolling statistics over multiple windows
  • External feature integration with lead/lag detection
  • Automatic feature importance filtering
  • Time series cross-validation
  • L1/L2 regularization to reduce overfitting risk

Real-World Use Case

Ideal when you have complex sales patterns influenced by multiple factors like marketing spend, seasonality, and economic indicators. Works exceptionally well with 50+ historical data points.

Prophet Short Series

Optimized for short time series with changepoint detection

Medium-High Complexity10+ Data Points✓ External Features

Overview

Facebook's Prophet algorithm, specially tuned for short time series. Excels at detecting sudden trend changes and capturing seasonality with limited historical data.

Best For:

Short series with sudden shifts (10-30 points)

Technical Specifications

Min Data Points:
10
Optimal Range:
10-30
External Features:
✓ Yes
Multi-Column:
✓ Yes
Confidence Intervals:
✓ Yes

Key Features

  • Piecewise linear trend with automatic changepoint detection
  • Additive or multiplicative seasonality (auto-selected)
  • Adaptive Fourier seasonality for user-defined frequencies
  • External regressors with optimal lag detection
  • 500 uncertainty samples for robust confidence intervals
  • Component decomposition (trend, seasonal, regressor effects)
  • Handles missing data and outliers gracefully

Real-World Use Case

Perfect for new products or markets with limited history. Captures sudden market shifts, promotional impacts, and seasonal patterns even with sparse data.

SARIMAX-Ridge

Statistical rigor meets controlled feature effects

Medium Complexity30+ Data Points✓ External Features

Overview

Combines classical SARIMAX statistical modeling with Ridge-regularized external features. Provides interpretable forecasts with explicit trend and seasonality components.

Best For:

Stable patterns with trend & seasonality (30-500 points)

Technical Specifications

Min Data Points:
30
Optimal Range:
30-500
External Features:
✓ Yes
Multi-Column:
✓ Yes
Confidence Intervals:
✓ Yes

Key Features

  • Automatic SARIMAX order selection via AIC/BIC
  • Ridge L2 regularization prevents feature stacking
  • Cross-correlation lag detection for features
  • Feature correlation filtering (removes redundancy)
  • Time series cross-validation for alpha selection
  • Bootstrap confidence intervals (200 samples)
  • Explicit trend and seasonal modeling

Real-World Use Case

Best for established products with clear seasonal patterns. Ideal when you need interpretable results and want to understand how external factors influence sales.

Theta-GAM

Enhanced decomposition with non-linear feature relationships

High Complexity24+ Data Points✓ External Features

Overview

Advanced hybrid combining Theta Method decomposition with Generalized Additive Models. Captures non-linear relationships between features and sales while maintaining interpretability.

Best For:

Non-linear patterns with feature interactions

Technical Specifications

Min Data Points:
24
Optimal Range:
50+
External Features:
✓ Yes
Multi-Column:
✓ Yes
Confidence Intervals:
✓ Yes

Key Features

  • STL decomposition for robust trend/seasonal extraction
  • Double-Theta logic with exponential trend damping
  • GAM for non-linear feature relationships
  • Automatic periodicity detection (ACF + FFT)
  • Automatic optimal lag detection via cross-correlation
  • Temporal cross-validation for lambda selection
  • Bootstrap confidence intervals

Real-World Use Case

Excellent for complex business scenarios where features have non-linear effects. Works well when external factors like pricing or competition have varying impacts over time.

Enterprise & Custom Solutions

Beyond Off-the-Shelf

When standard models aren't enough, we design and build custom forecasting solutions tailored to your specific business challenges.

Statistical Models

  • Custom ARIMA & state-space models
  • Hierarchical forecasting
  • Bayesian structural time series
  • Intermittent demand (Croston)

Machine Learning

  • Domain-specific feature engineering
  • Custom ensemble architectures
  • Gradient boosting pipelines
  • Automated hyperparameter tuning

Deep Learning

  • LSTM & GRU networks
  • Temporal Fusion Transformers
  • N-BEATS & N-HiTS
  • Custom attention mechanisms

What You Get

Dedicated Consultation

Deep-dive into your data, business context, and forecasting requirements

Flexible Deployment

Web app on Sanvia infrastructure or standalone solution running on your local servers

Ongoing Maintenance

Model monitoring, retraining, and continuous improvement as your data evolves

Interested in a custom solution?

support@sanvia.ai

Understanding Model Limitations

All forecasting models have inherent limitations and cannot predict the future with complete certainty. Forecast accuracy depends heavily on data quality, historical patterns continuing into the future, and the stability of underlying business conditions.

External features and economic indicators can enhance forecast accuracy, but models learn from historical correlations in your specific dataset. They cannot account for unprecedented events or sudden market disruptions.

We recommend using forecasts as one input in your decision-making process, combining them with domain expertise, market knowledge, and business judgment.

Try All Models with Your Data

Upload your sales data and compare all 4 models side-by-side. Our platform automatically optimizes each model for your specific data.

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Sales Forecasting Models | LightGBM, Prophet, SARIMAX, Theta-GAM | Sanvia