Time Series in Azure Platform

Having started the research on the Time Series Modeling with Multi-Agent technologies, it was decided to study how time-series forecasting is implemented in the various tools. To identify the features and options in Time Series Modelling, we are releasing cheat sheet at the end of each month. Currently, we have released version 2.1 and expected to release version 4 end of this month.

We have selected SQL Server, Azure, Weka, Orange, Rapid Miner and AWS platform to study the features of Time Series Modeling. We have already released the cheat sheet for SQL Server and this is to release the cheat sheet for Azure as shown below. 

There are three components in Azure with respect to Time Series, Azure Machine Learning Service, Azure Machine Learning and Azure Time Series Insight. Azure Machine Learning Service has rich features such as the ability to connect to different calendars of different countries, connect to public data sources, different normalization techniques, cross-validation techniques, and rich set of evaluation parameters. Further, it has the ability to configure exit conditions as well, so that time series will run into never-ending loops. 

In Azure Machine Learning, there is another control call Time Series Anomaly detection to detect the anomalies in time series and has the ability to replace the anomalies. 

References

 

https://azure.microsoft.com/en-gb/blog/build-more-accurate-forecasts-with-new-capabilities-in-automated-machine-learning/

https://docs.microsoft.com/en-gb/azure/machine-learning/tutorial-automated-ml-forecast

https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-forecast

https://docs.microsoft.com/en-us/archive/msdn-magazine/2017/november/machine-learning-azure-machine-learning-time-series-analysis-for-anomaly-detection

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/time-series-anomaly-detection