A Study on Demand and Sales Forecasting of Aviation Industry in India

This thesis investigates the application of time series analysis techniques in forecasting sales and demands, with a specific focus on their utilization within the retail sector. In today’s fast-paced and competitive business landscape, accurate forecasting plays a critical role in strategic decision-making, inventory management, and overall business performance. However, traditional forecasting methods often struggle to capture the complexities inherent in modern consumer behavior and market dynamics. Consequently, this research seeks to explore the efficacy of time series analysis, particularly methods such as ARIMA (AutoRegressive) Integrated Moving Average) and Exponential Smoothing, in providing more precise and reliable predictions. By analyzing historical sales data alongside relevant economic indicators, this study aims to uncover patterns, trends, and seasonality factors influencing sales and demands. The findings derived from this research endeavor to enhance forecasting practices within the retail industry, offering valuable insights to businesses seeking to optimize their operations and meet evolving customer expectations.