**Detecting stationarity in time series data** - Aug 20, 2019.

Explore how to determine if your time series data is generated by a stationary process and how to handle the necessary assumptions and potential interpretations of your result.

Tags: Forecasting, Stationarity, Time Series

**How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls** - May 10, 2019.

We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.

Tags: Forecasting, Machine Learning, Mistakes, Stationarity, Time Series

**Time Series for Dummies – The 3 Step Process** - Mar 5, 2018.

Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.

Tags: Data Science, Deep Learning, Machine Learning, Predictive Modeling, Stationarity, Time Series

**Introduction to Forecasting with ARIMA in R** - Jan 16, 2017.

ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.

Tags: ARIMA, Datascience.com, Forecasting, R, Stationarity, Time Series

**INRIA: PhD position, Learning with non-stationary data** - Feb 19, 2014.

Learning with non-stationary data - application to collaborative filtering and link prediction between name entities in knowledge bases like freebase.

Tags: France, INRIA, PhD, Stationarity