Complex Time Series Forecasting with Machine Learning Techniques

Create an advanced Excel workbook that implements multiple time series forecasting methods, including exponential smoothing, ARIMA, and regression-based predictions. Develop a flexible model that can handle seasonal variations, trend analysis, and potential outliers. Include statistical validation metrics, confidence intervals, and visual representations of predicted versus actual data. Demonstrate how this approach can be applied to sales forecasting, inventory planning, or resource allocation.