Business forecasting case study example is one of the popular case studies on YOU CANalytics. Originally, the time series analysis and forecasting for the case study were demonstrated on R in a series of articles. One of the readers, Anindya Saha, has replicated this entire analysis in Python. You could read this python notebook at this link: Python Notebook
So far we have covered the following topics in this case study example on time series forecasting and ARIMA models: Part 1 : Introduction to time series modeling & forecasting Part 2: Time series decomposition to decipher patterns and trends before forecasting Part 3: Introduction to ARIMA models for forecasting Part 4: ARIMA model case study example
This article is a continuation of our manufacturing case study example to forecast tractor sales through time series and ARIMA models. You can find the previous parts at the following links: Part 1 : Introduction to time series modeling & forecasting Part 2: Time series decomposition to decipher patterns and trends before forecasting Part 3: Introduction to ARIMA
For the last couple of articles, we are working on a manufacturing case study to forecast tractor sales for a company called PowerHorse. You can find the previous articles on the links Part 1 and Part 2. In this part, we will start with ARIMA modeling for forecasting. ARIMA is an abbreviation for Auto-Regressive Integrated Moving Average. However,
In the previous article, we started a new case study on sales forecasting for a tractor and farm equipment manufacturing company called PowerHorse. Our final goal is to forecast tractor sales in the next 36 months. In this article, we will delve deeper into time series decomposition. As discussed earlier, the idea behind time series decomposition is to extract
Today we are starting a new case study example series on YOU CANalytics involving forecasting and time series analysis. In this case study example, we will learn about time series analysis for a manufacturing operation. Time series analysis and modeling have many business and social applications. It is extensively used to forecast company sales, product demand,