TimesFM is a pretrained decoder-only transformer foundation model for time-series forecasting, developed by Google Research. The repository provides multiple model versions (v1.0, v2.0, v2.5, v3.0) with implementations in PyTorch, Flax/JAX, and MLX (Apple Silicon). It supports zero-shot univariate and multivariate forecasting with covariates, quantile outputs, and various preprocessing options (RevIN normalization, linear detrending, symmetric averaging). The codebase includes a high-level forecaster API, CLI scripts for CSV forecasting and system checks, fine-tuning examples (LoRA/DoRA), and comprehensive benchmarks (GIFT-Eval, TIME, FEV). While primarily an application library, it also contains research-oriented components like benchmark runners and notebooks, making it a hybrid project with a strong application focus.
This repository provides the official code, datasets, and checkpoints for the Timer family of large time series foundation models (Timer, Timer-XL, Sundial) from Tsinghua University. It implements a decoder-only Transformer architecture pre-trained on the Unified Time Series Dataset (UTSD) using the single-series sequence (S3) format. The code supports fine-tuning and evaluation on three downstream tasks: forecasting, segment-level imputation, and predictive anomaly detection. Data loading modules handle multiple time series formats (CSV, NPZ, TXT) with flexible windowing and scaling. The repository includes shell scripts for reproducible experiments across multiple datasets and data scarcity ratios. It also provides HuggingFace integration for zero-shot forecasting without training. While primarily a research codebase rather than a production application, it is well-structured with clean module separation and reusable components.
Time-MoE is a library/framework for training and deploying time series foundation models using a Mixture-of-Experts (MoE) architecture. It provides tools for training the TimeMoE model from scratch or fine-tuning pretrained versions, evaluating on benchmark datasets, and generating forecasts. The codebase is organized into modules for models, datasets, training utilities, and I/O operations, with a clear separation of concerns. Key features include efficient handling of large sequential datasets via sharded binary files, a custom MoE layer with load balancing loss, and integration with Hugging Face Transformers for model management and generation.
Kronos is a foundation model for financial K-line (OHLCV) time series data, accepted at AAAI 2026. It introduces a novel two-stage architecture: a BinarySphericalQuantizer tokenizer that converts continuous multi-dimensional market data into hierarchical discrete tokens, followed by an autoregressive decoder-only Transformer (with RoPE, RMSNorm, hierarchical embedding, and dependency-aware layers) pre-trained on data from 45+ global exchanges. The repository provides a complete research pipeline including model definition, tokenizer training, predictor fine-tuning (via Qlib or custom CSV), backtesting, and inference through multiple interfaces (Python API, CLI examples, a Tkinter GUI, and a Flask web UI). While the core model and tokenizer demonstrate strong research craftsmanship, the codebase exhibits signs of rapid prototyping: many example scripts with overlapping functionality, hardcoded paths, AI-generated comments, and prediction outputs that sometimes degenerate to constant values or numerical instability, indicating the model is still in an experimental stage. The repository is hybrid in nature—primarily a research project with application wrappers for demonstration and evaluation.