Time Series Analysis of Bikes Sales Dataset in JASP

This study examines the application of time series analysis to predict future bike sales, focusing on two categories: road and mountain bikes. Using the Prophet module in JASP, the research explores trends and weekly seasonality in sales data to derive insights into potential future performance. The study aims to enhance decision-making for inventory management and marketing strategies. A quantitative methodology underpins the research, relying on historical data and the Prophet forecasting tool. The findings reveal no seasonal patterns, different trends for each bike category, enabling targeted business strategies. Practical implications include improved forecasting accuracy and resource allocation. While limited to two bike categories and reliant on the dataset’s quality, the research demonstrates the utility of time series forecasting for retail applications. The originality lies in applying modern forecasting tools to a specific retail context, contributing to both academic literature and industry practice. Our expectations are to have peak sales during weekends when customers have more free time for sports and recreation for both categories, albeit with varying intensities. But weekly patterns are not revealed. The MAPE values (for both categories-mountain and road bikes) are quite high and they do not increase slightly within the time frame of the forecast period. The conclusion is that if a trend was identified the forecast would not be reliable.