All Videos Tagged Forecasting (Data Science Central) - Data Science Central 2021-04-12T15:11:47Z https://www.datasciencecentral.com/video/video/listTagged?tag=Forecasting&rss=yes&xn_auth=no DSC Webinar Series: No-code ML for Forecasting and Anomaly Detection tag:www.datasciencecentral.com,2020-06-02:6448529:Video:955494 2020-06-02T21:36:37.902Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-no-code-ml-for-forecasting-and-anomaly"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/5526174271?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>In this latest Data Science Central webinar, we will introduce and demonstrate how you can perform common time-series Machine Learning tasks such as Forecasting and Anomaly Detection, directly within the Influx platform without the need to use external tools, languages and services<br></br> <br></br> During this webinar, you will learn:…<br></br> <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-no-code-ml-for-forecasting-and-anomaly"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/5526174271?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />In this latest Data Science Central webinar, we will introduce and demonstrate how you can perform common time-series Machine Learning tasks such as Forecasting and Anomaly Detection, directly within the Influx platform without the need to use external tools, languages and services<br /> <br /> During this webinar, you will learn:<br /> <br /> How to initiate Machine Learning tasks directly within the Influx visual interface without intimate knowledge of how these algorithms are implemented<br /> How data scientists can wrap existing, or develop new, Machine Learning algorithms for publication to the Influx time-series platform using familiar languages and frameworks <br /> <br /> Speaker: <br /> Dean Sheehan, Field CTO - InfluxData<br /> <br /> <br /> Hosted by: <br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: How to Use Time Series Data to Forecast at Scale tag:www.datasciencecentral.com,2019-09-12:6448529:Video:887298 2019-09-12T20:55:33.580Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-use-time-series-data-to-forecast-at"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3553482484?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-use-time-series-data-to-forecast-at"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3553482484?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the analysis of a single data series of just tens of data points to large scale processing of thousands of time series and millions of data points.<br /> <br /> In this latest Data Science Central webinar, we will demonstrate how to leverage InfluxDB to implement some solutions to tackle on the issues of time series forecasting at scale, including continuous accuracy evaluation and algorithm hyperparameters optimization. As a real world use case, we will discuss the storage forecasting implementation in Veritas Predictive Insights which is capable of training, evaluating and forecasting over 70,000 time series daily.<br /> <br /> Speaker:<br /> Marcello Tomasini, Sr. Data Scientist - Veritas Technologies<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: ML vs Holt-Winters Forecasting tag:www.datasciencecentral.com,2019-07-25:6448529:Video:860979 2019-07-25T21:42:33.082Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-vs-holt-winters-forecasting"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3384820343?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare? In this latest Data Science Central webinar, Developer Advocate Anais Dotis-Georgiou will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-vs-holt-winters-forecasting"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3384820343?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare? In this latest Data Science Central webinar, Developer Advocate Anais Dotis-Georgiou will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast.<br /> <br /> Speaker:<br /> Anais Dotis-Georgiou, Developer Advocate - InfluxData<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central