Published in

Time Series Forecasting with Telegraf

If you’re familiar with Telegraf, you know that you can easily configure this lightweight collection agent with a single TOML configuration file to gather metrics from over 180 inputs and write data to a wide variety of different outputs and/or platforms. You might also know that Telegraf can act as a processor, aggregator, parser, and serializer. For example, you might even be familiar with the Starlark Processor Plugin that gives you the ability to perform various mathematical operations in Telegraf.




Data Scientists must think like an artist when finding a solution when creating a piece of code. ⚪️ Artists enjoy working on interesting problems, even if there is no obvious answer ⚪️ 🔵 Follow to join our 18K+ Unique DAILY Readers 🟠

Recommended from Medium

What is Data Science?

Effective DS Infra, MetaCards, Data Domains; ThDPTh #55

Marketing Mix Modelling (MMM) — Factors impacting MMM (Part 3)

5x a data team at Postman, 13 kinds of network effects, Meltano extends beyond ETL; ThDPTh #43

Dashboards Are Dead

The Importance of Business Intelligence to Small and Large Businesses

Gene network analysis completes the picture of bioproduction

A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Anais Dotis

Anais Dotis

Developer Advocate at InfluxData

More from Medium

Multivariate Time Series Forecasting with Deep Learning

Time Series Forecasting using TBATS Model

KerasBeats: An Easy Way to Use N-Beats in Keras

Probabilistic Forecasting in Darts