In previous posts I introduced very simple (and naive) forecasting methods, namely CAGR-based forecasting and simple moving average forecasting. I implemented such forecasting methods in R and demonstrated basic use cases. In this post I want to introduce another simple forecasting method: Random walk forecasting. I will implement an example using Python. Random walk forecasting […]
In a previous post I explained CAGR-based forecasting. CAGR-based forecasting is a very simple forecasting method which is often applied in industry, e.g. for forecasting sales and production output. Simple forecasting models have benefits. They are easy to understand and easy to implement. In addition, they contain few parameters and are thus very precise in their […]
In a previous post I downloaded a data set from kaggle containing data that had been scraped from ebay. The data set contained ebay postings on used car. The data was from Germany. My analysis considers cars registered at latest in 2015, since most of the data entries were crawled in 2016. The data can […]
In previous posts I have querried and visualized data related to US automotive industry, using public data provided by e.g. the Federal Reserve or the US bureau of labor statistics. As a further reference, I wanted to plot a graph on total US passenger car and light commercial vehicle sales, according to calsalesbase.com The data has […]
In previous articles I took a look at US automotive industry. I considered data collected by FRED and US bureau of labor statistics. The data showed strong declines in US automotive production output and US sales of imported vehicles – especially in most recent years. The data also showed that average hourly earnings of employees […]