Time-Series Analysis For Digital Analytics in R (Pt. 2)

Introduction Time-series analysis is a powerful way to predict events that occur at a future time. In this post I am going to be following up from two previous posts (Pt 1 ā€“> https://rebrand.ly/l1tf4p1 and Introduction to Time-Series ā€“> https://rebrand.ly/z4lomwu) and Iā€™m going to mainly use the forecast package to explore an ARIMA time-series model in R. Retrieving … Read more

Introduction of Time Series Analysis For Digital Analytics

time series

Let’s say you want to forecast future revenue. You could fill in a month-to-month or week-to-week spreadsheet template. However, these are very subjective methods of trying to predict the future and often wildly inaccurate.

A better method would be to create predictive models using time series analysis. Time series predictive models are a specific subset of statistical models that deal with data that are ordered by and dependent on time. On a graph, the x-axis would be time, with the y-axis the thing (revenue, in this case) you are measuring.

In this post, I will give an overview of one type of time series models and describe how they work in simple language. I will describe how to implement the models in BigQuery ML. 

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