Exponential Smoothing Methods: Modeling and Forecasting Hotel Data

This report focuses on modeling and forecasting using Exponential Smoothing Methods (ESM) on a Denmark hotel dataset. It aims to identify the most appropriate exponential smoothing method for the given data series and interpret the values in the seasonal exponential smoothing parameter estimates. The analysis likely involves applying various ESM techniques, such as simple exponential smoothing, Holt's linear trend method, and Holt-Winters' seasonal method, to the hotel data. The selection of the most suitable method would be based on performance metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). Furthermore, the report would delve into the seasonal parameter estimates to understand the underlying seasonal patterns within the hotel data, providing insights into the factors influencing hotel occupancy and demand in Denmark. The report also references several academic papers on forecasting and statistical modeling to support its methodology and findings.

MODELING AND FORECASTING
NAME: AA
PROFF: DD
7/24

MODELING AND FORECASTING USING ESM

DENMARK HOTEL DATA-SET RESULT

DENMARK HOTEL DATA-SET
RESULT

MODELING AND FORECASTING CODE

MODELING AND FORECASTING RESULT

MODELING AND FORECASTING RESULT

MODELING AND FORECASTING RESULT

MODELING AND FORECASTING RESULT

MODELING AND FORECASTING RESULT

MODELING AND FORECASTING RESULT

MODELING AND FORECASTING RESULT