Time series are ubiquitous in real-world applications, but often add considerable complications to data science workflows. What’s more, most available machine learning toolboxes (e.g. scikit-learn) are limited to the tabular setting, and cannot easily be applied to time series data.

In this tutorial, you’ll learn how to apply common machine learning techniques to time series and how to extend available toolkits. This is a beginner-friendly tutorial: we assume familiarity with scikit-learn, but no prior experience with time series.

To start, you’ll learn how to distinguish between different kinds of temporal data and associated learning tasks, such as forecasting and time series classification. You’ll then learn how to solve these tasks with machine learning techniques specific to time series data. 

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.

Time index:

  • [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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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.     

Try Azure Time Series Insights today: https://aka.ms/tsipreview
Check out the JS SDK for TSI in action: https://aka.ms/tsiclientdemos
Try Azure IoT for free today: https://aka.ms/aft-iot

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