Tag: Time Series
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Capacity Forecasting From Infrastructure Telemetry (Infra to Data Science Series, Part 22)
A straight line on a seasonal metric tells you when the average crosses your limit, not when the weekly peak does. Forecast with Holt-Winters, read the date off the upper prediction band, and plan against the risk you can afford.
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Anomaly Detection on Metrics and Time Series for Infra Telemetry (Infra to Data Science Series, Part 21)
Static thresholds on seasonal infrastructure metrics measure the time of day, not trouble. Deseasonalise with STL, score the residual with a median and MAD based z, and reach for an isolation forest when several metrics move together.
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Infrastructure Telemetry as a Dataset for Data Science (Infra to Data Science Series, Part 7)
Your monitoring data already is a dataset. This part reframes infrastructure telemetry as rows and columns, and shows why a counter has to become a rate before it can be a feature.
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Time Series Forecasting in Python, and Why Your Train Test Split Is Wrong (Data Science Series, Part 19)
A shuffled cross validation split on time ordered data will hand you a score you cannot ship. Here is what an honest forecast split looks like, how to backtest with a moving origin, and how far ahead a model is actually good for.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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