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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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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Amazon Forecast
Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts.
Companies today use everything from simple spreadsheets to complex financial planning software to attempt to accurately forecast future business outcomes such as product demand, resource needs, or financial performance. These tools build forecasts by looking at a historical series of data, which is called time series data. For example, such tools may try to predict the future sales of a raincoat by looking only at its previous sales data with the underlying assumption that the future is determined by the past. This approach can struggle to produce accurate forecasts for large sets of data that have irregular trends. Also, it fails to easily combine data series that change over time (such as price, discounts, web traffic, and number of employees) with relevant independent variables like product features and store locations.
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Seeq
Seeq is the first application dedicated to process data analytics. Search your data, add context, cleanse, model, find patterns, establish boundaries, monitor assets, collaborate in real time, and interact with time series data like never before. Whatever your process historian or operational data system of record – the OSIsoft® PI System®, Honeywell's Uniformance® PHD, Emerson DeltaV and Ovation, Inductive Automation's Ignition, AspenTech IP.21, Wonderware, GE Proficy or any other – Seeq can connect and get you working in minutes. In the current hype around predictive analytics, machine learning, and data science, what’s missing are solutions to the real challenges to an analytics-driven organization. Tapping the expertise of your current employees. Support for collaboration and knowledge capture to foster sharing and reuse of analytics efforts. And the ability to rapidly distribute insights to the people who need them to quickly improve outcomes.
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