As a Data Analyst in my previous role at XYZ Telecom, I frequently dealt with time-series analysis for business forecasting purposes. One notable use of ARIMA I can recall was predicting the future demand for various telecom services.
Given the seasonal nature of some services, it was vital to make accurate predictions to optimize resource allocation. For this, our team decided to use the ARIMA model, given its efficacy in dealing with non-stationary data with trends and seasonality. My role involved cleaning the dataset, ensuring the stationarity of the time series, and then using those to train our ARIMA model. The entire process was implemented using R and its 'forecast' package.
Upon implementation, our predictions were quite accurate, helping the company proactively manage resources, thereby improving customer service. This was one of my key projects where I was able to apply theoretical knowledge to solve a real-world business problem using ARIMA.