Andere Kunden interessierten sich auch für
![Gegenüberstellung von Forecast-Modellen für den Seefrachttransport. Analyse des Trades zwischen Brasilien und Europa]( "Gegenüberstellung von Forecast-Modellen für den Seefrachttransport. Analyse des Trades zwischen Brasilien und Europa")
Gegenüberstellung von Forecast-Modellen für den Seefrachttransport. Analyse des Trades zwischen Brasilien und Europa18,95 €![Modelling and forecasting monthly petroleum prices of Ghana using subset ARIMA models]( "Modelling and forecasting monthly petroleum prices of Ghana using subset ARIMA models")
Modelling and forecasting monthly petroleum prices of Ghana using subset ARIMA models39,99 €![Forecasting Macroeconomic Variables by Using Model Averaging]( "Forecasting Macroeconomic Variables by Using Model Averaging")
Forecasting Macroeconomic Variables by Using Model Averaging23,99 €![Do Macroeconomic Variables have an Effect on the US Stock Market?]( "Do Macroeconomic Variables have an Effect on the US Stock Market?")
Do Macroeconomic Variables have an Effect on the US Stock Market?18,95 €![The driving forces and innovation process of Swedish eco-innovation and food-tech firm]( "The driving forces and innovation process of Swedish eco-innovation and food-tech firm")
The driving forces and innovation process of Swedish eco-innovation and food-tech firm17,95 €![Corporate Social Responsibility with regard to Eco Seals and Garment Disposal]( "Corporate Social Responsibility with regard to Eco Seals and Garment Disposal")
Corporate Social Responsibility with regard to Eco Seals and Garment Disposal17,95 €![Total Cost of Ownership Comparison for Different Drivetrains of Private Transport Vehicles]( "Total Cost of Ownership Comparison for Different Drivetrains of Private Transport Vehicles")
Total Cost of Ownership Comparison for Different Drivetrains of Private Transport Vehicles47,95 €
Project Report from the year 2013 in the subject Economics - Statistics and Methods, grade: 1,0, , course: ECO 309, language: English, abstract: For this project I created a twelve month forecast for Total Vehicle Sales in the United States using four different methods. These four techniques are called exponential smoothing, decomposition, ARIMA, and multiple regression. To do so I picked one dependent (Y) variable along with two independent (X) variables and collected 80 monthly observations for each variable. This historical data allowed me to create four different forecasting models which predict future Vehicle Sales with low risk of error. The best model according to the lowest error measures was winter's exponential smoothing method because it had the lowest MAPE along with the lowest RMSE for the fit period as well as the forecast period.