Business Demand Forecasting Using Numerical Interpolation and Curve Fitting

Abstract

In Today's Corporate Contexts, Strategy Planning And Resource Allocation Depend Heavily On Accurate Business Demand Forecasts. When Dealing With Irregular Datasets And Non-Linear Demand Patterns, Traditional Approaches Often Fail. This Paper Explores The Use Of Curve Fitting And Numerical Interpolation As Reliable Mathematical Methods To Improve Forecasting Accuracy In Commercial Settings With Seasonality And Dynamic Customer Behaviour. In Order To Predict Business Demand More Accurately, The Study Combines Newton's Divided Difference Interpolation, Lagrange Interpolation, And Least Squares Curve Fitting Techniques. The U.S. Census Bureau's Monthly Retail Trade Report, Which Focusses On Monthly Sales Over A Five-Year Period, Provides A Real-World Retail Dataset That Is Used To Verify These Mathematical Models. Advanced Interpolation And Fitting Methods Perform Noticeably Better Than Traditional Linear Regression Procedures, According To Numerical Testing, Especially When Dealing With Missing Or Fluctuating Data Points. The Findings Demonstrate That These Numerical Techniques Provide A Flexible, Dependable, And Reasonably Priced Framework For Demand Forecasting, With Immediate Ramifications For Supply Chain Optimization, Inventory Control, And Marketing Strategy.