AI Time Series Analysis platforms process and analyze temporal data patterns using artificial intelligence. These tools provide capabilities for trend analysis, forecasting, and anomaly detection in time-based data.
16 verified AI-first sites in Time Series Analysis.
Time series foundation model company behind TimeGPT and the open-source Nixtlaverse (StatsForecast, MLForecast, NeuralForecast). TimeGPT-2.1 and Nixtla Enterprise add multivariate and agentic forecasting plus anomaly detection; raised a $16M Series A in February 2026.
Pricing: Open-source libraries free; TimeGPT API free trial with usage-based and enterprise plans.
Automated time series forecasting and anomaly detection engine that builds and rebuilds models on the fly without manual tuning. Available via API and as a Databricks runtime for high-throughput enterprise workloads.
Pricing: Custom enterprise pricing; contact Tangent.
Amazon open-source Python library for probabilistic time series forecasting with deep learning. Ships DeepAR, Transformer, and other models with training, backtesting, and evaluation tooling for univariate and multivariate series.
Pricing: Open source: Free.
Open-source PyTorch forecasting framework from Stanford that extends Prophet with neural components. Models trend, seasonality, holidays, lagged values, and external regressors with interpretable outputs; development has been quiet since early 2025.
Pricing: Open source: Free.
AI business monitoring platform that learns normal behavior across time-series metrics and flags anomalies in revenue, payments, and operations in real time. Acquired by Glassbox in November 2025; still sold as a standalone product.
Pricing: Enterprise pricing with platform plans and integrations; contact sales.
Unified API for time series foundation models including Chronos, TimesFM, and Moirai. Zero-shot forecasting, prediction intervals, anomaly detection, and imputation without provisioning GPUs or managing model weights.
Pricing: Free tier; see site for API pricing.
Open-source GenAI forecasting agent that combines LLMs with 30+ time-series foundation models (Chronos, Moirai, TimesFM, TimeGPT, and more). Natural-language forecasts, cross-validation, and anomaly detection through one API or CLI.
Pricing: Free and open source; LLM and model API keys as needed.
IBM Granite FlowState time series foundation model extending TinyTimeMixer-style efficiency to richer workloads. Compact pretrained checkpoint for zero-shot and few-shot forecasting on Hugging Face.
Pricing: Open weights on Hugging Face; see IBM Granite license.
Amazon time series foundation model for zero-shot forecasting. Chronos-2 handles univariate, multivariate, and covariate-informed tasks with strong benchmark results and SageMaker deployment options.
Pricing: Open weights on Hugging Face; AWS SageMaker and AutoGluon Cloud deployment.
Google Research time series foundation model, available natively in BigQuery ML for zero-shot AI.FORECAST and anomaly detection in SQL. TimesFM-3 (August 2026) adds native multivariate forecasting and covariates, with open weights on Hugging Face.
Pricing: Included with BigQuery usage; open weights also on Hugging Face.
Salesforce universal time series foundation model for zero-shot forecasting across domains, frequencies, and variable counts. Moirai 2.0 improves accuracy and speed on GIFT-Eval versus earlier releases.
Pricing: Open weights on Hugging Face; Uni2TS tooling for inference.
Datadog time series foundation model family for multivariate forecasting, tuned on observability metrics. Toto 2.0 scales from 4M to 2.5B parameters with strong GIFT-Eval and BOOM results.
Pricing: Open Apache 2.0 weights on Hugging Face.
European lab behind TiRex time-series models built on xLSTM memory architectures, including TiRex-2. Targets industrial forecasting and edge or embedded deployment beyond transformer baselines.
Pricing: Research and partnership programs; contact NXAI.
Open time series foundation model family from CMU AutonLab for forecasting, classification, anomaly detection, and imputation. Pretrained on the Time Series Pile with weights on Hugging Face.
Pricing: Open weights on Hugging Face; Apache-licensed research code.
Mistral's first family of time series foundation models for zero-shot forecasting, released in October 2026 in 5M, 28M, 123M, and 209M parameter sizes. It produces probabilistic multivariate forecasts across 21 quantiles, accepts past and known-future covariates, and handles up to 32,768 context steps on CPU or GPU. Mistral reports the 209M model ranks first on GIFT-Eval, TIME, and fev-bench, with weights and code published under a modified MIT license.
Pricing: Free open weights (modified MIT license) on Hugging Face and GitHub.
Research lab building the t0 family of forecasting foundation models, which read raw history plus related series and known-future inputs and return calibrated probabilistic forecasts. Its open-weights t0-gamma, released in October 2026, is reported as first on fev-bench, GIFT-Eval, and TIME at release, and runs locally through the open-source tfc-t0 library or through a hosted API. Requires no feature engineering or fine-tuning, and the Retrocast tool backtests a model against every series in a dataset.
Pricing: Open weights on Hugging Face; hosted API access via the company site.