Nixtla
Nixtla is a platform for time-series forecasting and anomaly detection built around its flagship model TimeGPT, described as the first generative AI foundation model for time-series data. It was trained on over 100 billion data points spanning domains such as retail, energy, finance, IoT, healthcare, weather, web traffic, and more, allowing it to make accurate zero-shot predictions across a wide variety of use cases. With just a few lines of code (e.g., via their Python SDK), users can supply historical data and immediately generate forecasts or detect anomalies, even for irregular or sparse time series, and without needing to build or train models from scratch. TimeGPT supports advanced features like handling exogenous variables (e.g., events, prices), forecasting multiple time-series at once, custom loss functions, cross-validation, prediction intervals, and model fine-tuning on bespoke datasets.
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Azure AI Anomaly Detector
Foresee problems before they occur with an Azure AI anomaly detection service. Easily embed time-series anomaly detection capabilities into your apps to help users identify problems quickly. AI Anomaly Detector ingests time-series data of all types and selects the best anomaly detection algorithm for your data to ensure high accuracy. Detect spikes, dips, deviations from cyclic patterns, and trend changes through both univariate and multivariate APIs. Customize the service to detect any level of anomaly. Deploy the anomaly detection service where you need it, in the cloud or at the intelligent edge. A powerful inference engine assesses your time-series dataset and automatically selects the right anomaly detection algorithm to maximize accuracy for your scenario. Automatic detection eliminates the need for labeled training data to help you save time and stay focused on fixing problems as soon as they surface.
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Amazon Lookout for Metrics
Reduce false positives and use machine learning (ML) to accurately detect anomalies in business metrics. Diagnose the root cause of anomalies by grouping related outliers together. Summarize root causes and rank them by severity. Seamlessly integrate AWS databases, storage services, and third-party SaaS applications to monitor metrics and detect anomalies. Automate customized alerts and actions when anomalies are detected. Automatically detect anomalies within metrics and identify their root causes. Lookout for Metrics uses ML to detect and diagnose anomalies within business and operational data. Detecting unexpected anomalies is challenging since traditional methods are manual and error-prone. Lookout for Metrics uses ML to detect and diagnose errors within your data, with no artificial intelligence (AI) expertise required. Identify unusual variances in subscriptions, conversion rates, and revenue, so you can stay on top of sudden changes.
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Google Cloud Inference API
Time-series analysis is essential for the day-to-day operation of many companies. Most popular use cases include analyzing foot traffic and conversion for retailers, detecting data anomalies, identifying correlations in real-time over sensor data, or generating high-quality recommendations. With Cloud Inference API Alpha, you can gather insights in real-time from your typed time-series datasets. Get everything you need to understand your API queries results, such as groups of events that were examined, the number of groups of events, and the background probability of each returned event. Stream data in real-time, making it possible to compute correlations for real-time events. Rely on Google Cloud’s end-to-end infrastructure and defense-in-depth approach to security that’s been innovated on for over 15 years through consumer apps. At its core, Cloud Inference API is fully integrated with other Google Cloud Storage services.
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