This is a post to link together various past/future ones into a single story.
- Understanding time series and various concepts like stationary, seasonality, trend and additive/multiplicative series.
- Forecasting with Facebook Prophet
- Forecasting with SARIMA
- Forecasting with LinkedIn Greykite
- Trying out Auto-ML with AWS and GCP
- How to understand and explain accuracy of forecasts
There will probably be some more related posts over time and I will try to keep this index updated!
“Software does not fail” is a provocative statement and your initial reaction maybe to dismiss it. But if we look deeper, software “failure” is never random compared to possible random failure of hardware components (refer to Dr Nancy Leveson, YouTube, Engineering a Safer World). Yes, there are hard to reproduce…
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So far I have looked into understanding time series and then forecasting with Prophet, SARIMA, Greykite and Auto-ML with AWS and GCP. This is now the final part to understand how to evaluate and explain accuracy of forecasts.
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Following up from some recent time series forecasting with Prophet, SARIMA and Greykite, this is a fourth related topic and I looked into Amazon Forecast from AWS and the GCP Vertex AI forecasting. …
As a machine learning learner one important topic to learn is producing recommendations. The Netflix prize is cited everywhere but unfortunately learning resources are simple examples or very complex research papers. Here is my attempt to curate some resources to understand various types of recommendations and keeping a new learner…
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I have been learning about time series and recently looked into Facebook Prophet and SARIMA. There is now a new Greykite library from LinkedIn that seeks to create an abstraction over multiple models including Prophet and introduces their own model called Silverkite.
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Previously I had looked into Facebook Prophet and this is now an attempt to learn moving average (MA) models and ARIMA. …
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If you are monitoring your web services with Wavefront, Prometheus, Grafana etc. then you are aware of time series. In this post I am sharing my learnings about time series forecasting. …
Getting started from an excellent intro from Charlie Flanagan and his Machine Learning for Business class here is some experimentation with my own models, Google AutoML and AWS SageMaker.
Problem
Product reviews from a women’s clothing ecommerce store are provided. Based on each review it has been classified as whether the…
In a recent presentation about API Gateways, I got a question about service mesh and I did not understand the question or have a good answer. So here is an attempt to understand these concepts and see how they fit together in the context of a typical container based microservice…
