Comparison between Double and Triple Exponential Smoothing Methods for Forecasting Poverty Rate of Yahukimo Regency in 2025-2030

Forecasting with exponential smoothing methods and bootstrap

The Boot.EXPOS procedure is an algorithm that combines the use of exponential smoothing methods with the bootstrap methodology for obtaining forecasts. In previous works the authors have studied and analyzed the interaction between these two methodologies. The initial sketch of the procedure was developed, modified and evaluated until its final form designated as Boot.EXPOS.

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

investigation of single-user and multi-user detection methods in mc-cdma systems and comparison of their performances

در این پایان نامه به بررسی روش های آشکارسازی در سیستم های mc-cdma می پردازیم. با توجه به ماهیت آشکارسازی در این سیستم ها، تکنیک های آشکارسازی را می توان به دو دسته ی اصلی تقسیم نمود: آشکارسازی سیگنال ارسالی یک کاربر مطلوب بدون در نظر گرفتن اطلاعاتی در مورد سایر کاربران تداخل کننده که از آن ها به عنوان آشکارساز های تک کاربره یاد می شود و همچنین آشکارسازی سیگنال ارسالی همه ی کاربران فعال موجود در...

Short-term electricity demand forecasting using double seasonal exponential smoothing

This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the ...

a comparison between time series, exponential smoothing, and neural network methods to forecast gdp of iran