Nekustamo īpašumu tirgus cenu prognozēšanas modeļi
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Latvijas Universitāte
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lav
Abstract
Bakalaura darbā ir aplūkotas daudzfaktoru lineāra regresija, vispārējie aditīvi modeļi, regresiju koku metode nekustamo īpašumu tirgus cenu prognozēšanā un analīzē. Visi trīs modeļu veidi tiks salīdzināti savā starpā, aplūkojot katra modeļa nekustamo īpašumu tirgus cenu prognožu precizitātes, analizējot prognožu kļūdu mērus, tādus kā MSE, RMSE, MAE, MAPE. Analīzes rezultātā tiks noteikts labākais modelis nekustamo īpašumu cenu prognozēšanai.
The study examines multifactor linear regression, generalized additive models, and regression tree methods for forecasting and analyzing real estate market prices. All three model types will be compared with each other, overwieving accuracy of each model’s real estate market price forecasts by analyzing forecast error measures such as MSE, RMSE, MAE, and MAPE. As a result of the analysis, the best model for forecasting real estate prices will be determined.
The study examines multifactor linear regression, generalized additive models, and regression tree methods for forecasting and analyzing real estate market prices. All three model types will be compared with each other, overwieving accuracy of each model’s real estate market price forecasts by analyzing forecast error measures such as MSE, RMSE, MAE, and MAPE. As a result of the analysis, the best model for forecasting real estate prices will be determined.