forecasting – paulvanderlaken.com

Tensorflow is a open-source machine learning (ML) framework. It’s primarily used to build neural networks, and thus very often used to conduct so-called deep learning through multi-layered neural nets.

Although there are other ML frameworks — such as Caffe or Torch — Tensorflow is particularly famous because it was developed by researchers of Google’s Brain Lab. There are widespread debates on which framework is best, nonetheless, Tensorflow does a pretty good job on marketing itself.

Google search engine searches on *Tensorflow* in comparison to searches on *Machine learing* and *Deep learning*

I primarily work in the programming language R, and have written before about how to start with deep learning in R using Keras — an user-friendly API built on top of, among others, Tensorflow. Now, it has become even easier to learn how to implement the power of Tensorflow in R, for RStudio has compiled a gallery of featured posts on Tensorflow implementations in R. It features a variety of applications related to collaborative filtering, image recognition, audio classification, times series forecasting, and fraud detection, all using Keras and TensorFlow. I highly recommend you check it out if you want to learn more about deep learning in R.

I stumbled across this open access book by Rob Hyndman, the god of time series, and George Athanasopoulos, a colleague statistician / econometrician at Monash University in Melbourne Australia.

Hyndman and Athanasopoulos provide a comprehensive introduction to forecasting methods, accessible and relevant among others for business professionals without any formal training in the area. All R examples in the book assume work build on the fpp2 R package. fpp2 includes all datasets referred to in the book and depends on other R packages including forecast and ggplot2.

Some examples of the analyses you can expect to recreate, ignore the agricultural topic for now ; )

Monthly milk production per cow. — One of the example analysis you will recreate by following the book (Figure 3.3)

Forecasts of egg prices using a random walk with drift applied to the logged data. — You will be forecasting price data using different analyses and adjustments (Figure 3.4)

I highly recommend this book to any professionals or students looking to learn more about forecasting and time series modelling. There is also a DataCamp course based on this book. If you got value out of this free book, be sure to buy a hardcopy as well.