Here’s a deep dive into Azure Time Series Insights, a serverless, fully Managed Data Analytics Solution (PaaS) Built for IoT – TSI provides best-in-class IoT analytics platform that empowers customers across all verticals to analyze, visualize and monitor business outcomes.
Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand.
Building machine learning models can be a time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering.
These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data.
Forecasting within automated machine learning (ML) takes these factors into consideration and includes capabilities that improve the accuracy and performance of our recommended models.
This session will highlight the forecasting features of Automated ML and how to leverage them.
- [00:35] – What is time-series forecasting?
- [01:30] – Simplify ML with Automated ML
- [02:30] – DriveTime customer scenario
- [04:15] – Features & Functionality
- [05:20] – Demo
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Learn about the new features Automated Machine Learning (Automated ML) is releasing, from enhanced time-series forecasting capabilities and no-code user interface, to DNN support for text and forecasting scenarios.
You can now derive rich insights from your IoT data in Azure Time Series Insights using advanced visualization options.
The team has introduced several new capabilities into TSI Explorer since we launched last December. These include significant Performance improvements, new Explorations like Scatter Plots & Heatmaps, as well as an enhanced JS SDK and more. Rahul Kayal, PM in the TSI team walks us through the latest additions and enhancements in TSI.
Check this video out to learn more.
Time series is the fastest growing category of data out there! It’s a series of data points indexed in time order.
Often, a time series is a sequence taken at successive equally spaced points in time. In this video, Siraj Raval covers 8 different time series techniques that will help us predict the price of gold over a period of 3 years.
Code for this video https://github.com/llSourcell/Time_Series_Prediction