Prophet for Business Forecasting

Business time series data are everywhere. Can we come up with 20 examples for business time series data? Let’s play a popular game Family Feud to find out. Family Feud is a game show where two teams compete to guess the most popular answers to survey questions within a time limit. I can think of retail, manufacturing, phone services, and airline traffic for business time series data. In retail, there is daily and weekly sales volume. There is daily and weekly store traffic. And there are daily inventory levels. In manufacturing, there are daily, weekly, or monthly production volumes. In airline traffic, the numbers of passengers per day are business time series data. If it is a cargo airline, shipment volumes over time. Well. I think that’s enough to score the Family Feud game. You probably get my point — business time series data are everywhere. Analyzing and forecasting these time series is critical for data scientists. When we analyze the past patterns of business time series to forecast the future, the forecasts can provide valuable insights for decision-making, resource planning, and performance optimization within organizations.

Business time series data exhibit seasonality and trend patterns. Seasonality is recurring patterns that follow a regular schedule such as days, weeks, or months. Business time series also have underlying trends that show long-term tendencies. Trends can be…