Several open-source AI models have emerged as leading solutions for time series forecasting, each excelling in different scenarios based on architecture, training data, and use case requirements. The most prominent options include foundation models like TimesFM (Google Research) and Lag-Llama, transformer-based solutions such as Chronos (Amazon) and TimeGPT (Nixtla), and specialized libraries like Darts and Prophet (Meta). Performance varies significantly: TimesFM demonstrates zero-shot capabilities with 100 billion pre-trained time-points ^[5], while Lag-Llama offers probabilistic forecasting without frequency dependencies ^[4]. For enterprise applications, transformer models like Chronos and TimeGPT provide scalable solutions with pre-training advantages, whereas traditional libraries like Prophet remain popular for their robustness with messy data ^[7].

Key findings from the search results:

• Top foundation models: TimesFM (Google) leads in zero-shot performance with 100B+ pre-trained points ^[5], while Lag-Llama specializes in univariate probabilistic forecasting ^[4]
• Transformer-based solutions: Chronos (Amazon) and TimeGPT (Nixtla) leverage generative AI for scalable forecasting, with TimeGPT offering a GPT-like architecture for time series ^[1][2]
• Specialized libraries: Darts provides 30+ models (ARIMA, LSTM, etc.) for comparison ^[3], while Prophet (Meta) excels with seasonal data and missing values ^[7]
• Performance metrics: Models are evaluated on MAE/RMSD, with transformer-based solutions generally outperforming statistical methods ^[1][5]

Open-Source AI Models for Time Series Forecasting

Foundation Models for Zero-Shot and Probabilistic Forecasting

The most advanced open-source solutions for time series forecasting now include foundation models that eliminate traditional training requirements. TimesFM from Google Research stands out as the largest pre-trained model, having processed 100 billion real-world time-points across diverse domains like retail, finance, and healthcare ^[5]. Its decoder-only transformer architecture enables zero-shot forecasting—delivering accurate predictions without fine-tuning on specific datasets. Evaluation results show TimesFM matching or exceeding deep learning models with fewer parameters, particularly in scenarios requiring rapid deployment across multiple time series ^[5].

For probabilistic forecasting needs, Lag-Llama provides a specialized alternative. Developed through academic collaboration, this model uses a frequency-agnostic tokenization method that adapts to any time series granularity (secondly, hourly, daily data) without preprocessing ^[4]. Key advantages include:

• Univariate probabilistic forecasts with quantile predictions ^[4]
• General-purpose architecture that doesn't require frequency specification ^[4]
• Open-source implementation with Python integration for research applications ^[4]
• Contrasted with TimeGPT as a fully open alternative to proprietary solutions ^[4]

Both models represent significant advancements over traditional approaches. TimesFM's massive pre-training dataset enables broad applicability, while Lag-Llama's design focuses on research flexibility. The choice depends on whether zero-shot capabilities (TimesFM) or probabilistic flexibility (Lag-Llama) is prioritized.

Transformer-Based Models and Specialized Libraries

Transformer architectures have revolutionized time series forecasting by enabling models to capture long-range dependencies in sequential data. Chronos from Amazon represents this new class of generative AI models, designed specifically for enterprise forecasting needs ^[2]. As part of Amazon's time series foundation models, Chronos takes a "naive" approach that simplifies traditional forecasting pipelines while maintaining high accuracy ^[2]. The model excels in scenarios requiring:

• Integration with cloud-based data platforms like Databricks ^[2]
• Handling of external variables alongside temporal patterns ^[2]
• Scalability for large-scale enterprise deployments ^[2]

• StatsForecast: Optimized for speed and statistical accuracy ^[1]
• NeuralForecast: Neural network-based models for complex patterns ^[1]
• SQL integration through MindsDB for database-native forecasting ^[1]

For organizations needing traditional model comparisons, Darts provides a unified Python library with 30+ implemented models including:

• Classical statistical methods (ARIMA, Exponential Smoothing) ^[3]
• Deep learning approaches (LSTM, N-BEATS) ^[3]
• Ensemble capabilities for model comparison ^[3]

Meanwhile, Prophet (Meta) remains a popular choice for its robustness with messy data, offering:

• Automatic seasonality detection and holiday effects handling ^[7]
• Missing data tolerance without requiring interpolation ^[7]
• Tunable parameters through an intuitive Python/R interface ^[7]

The Time-Series Library (TSLib) from THUMT provides another comprehensive option, featuring:

• State-of-the-art models like TimesNet and iTransformer ^[8]
• Support for exogenous variables through TimeXer ^[8]
• Benchmark leaderboards for model selection ^[8]
• Research-focused implementations with reproducible experiments ^[8]