List of Articles Exponential smoothing

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  1 - The predictive ability and information content of aggregate earnings beyond disaggregate earnings
  R. Shabahang Z. Lashgari
  The predictive ability and explanatory power of an aggregate model of reported earnings is compared to adisaggregated model of reported earnings by examining their associations with contemporaneous stockreturns and future earnings. The change in annual reported earnings More

  The predictive ability and explanatory power of an aggregate model of reported earnings is compared to adisaggregated model of reported earnings by examining their associations with contemporaneous stockreturns and future earnings. The change in annual reported earnings is disaggregated into changes in revenue,operating margin, and other expenses in order to examine whether they individually and jointly conveyinformation about future earnings that is not reflected in aggregate earnings and whether this incrementalpredictive ability is reflected in security prices.This study contributes to the information content literature in two ways. First, it examines the informationand valuation link between the operating income components of reported earnings and future earnings andstock returns. Second, the empirical analysis is conducted on an industry basis and over a eight-year periodto examine how the predictive ability and information content of earnings and its components vary acrossindustries and over time.The results indicate the following. First, changes in revenue, operating margin, and other expenses (thedisaggregated model) jointly have not predictive ability and information content beyond the change inaggregate reported earnings with respect to one-year ahead annual earnings and contemporaneous annualstock returns. Second, the predictive ability and, information content of these earnings components as well asaggregate earnings varies across industries and varies over time Manuscript profile
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  2 - Forecasting Seasonal and Trend-Driven Data: A Comparative Analysis of Classical Techniques
  Zahira MARZAK Rajaa BENABBOU Salma MOUATASSIM Jamal BENHRA
  10.22094/joie.2023.1984123.2057
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  3 - Short-Term Load Forecasting of Distribution Power System for Weekdays Using Old Data
  Bahador Fani Soleyman Fehresti Sani Ehsan Adib
  Estimation of daily load in distribution companies which is performed to present the results to the DMS, is necessary. Daily load forecasting of power systems has traditionally been considered. Because load patterns are influenced by several factors such as climate, eco More

  Estimation of daily load in distribution companies which is performed to present the results to the DMS, is necessary. Daily load forecasting of power systems has traditionally been considered. Because load patterns are influenced by several factors such as climate, economy and society, it is difficult to predict the load exactly. That's why in recent years the use of intelligent algorithms to predict it, is growing. In this project, the short-term load forecasting is performed in a hybrid approach. Due to the different behavior in different days, various methods have been used to predict the load. With studying different methods of load prediction, finally, finally exponential smoothing algorithm was used to predict the exact load in the weekdays. Manuscript profile
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  4 - A new adaptive exponential smoothing method for non-stationary time series with level shifts
  Mohammad Ali Saniee Monfared Razieh Ghandali Maryam Esmaeili
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  5 - Proposition of a model For Forecasting Value at Risk in One Step Ahead
  ehsan Mohammadian Amiri S. Babak Ebrahimi maryam Nezhad Afrasiabi
  Risk forecasting for future periods plays an important role in making the right decisions of managers and financial activists to invest in companies and institutions. On the other hand wrong decisions of commercial managers can have undesirable consequences for their or More

  Risk forecasting for future periods plays an important role in making the right decisions of managers and financial activists to invest in companies and institutions. On the other hand wrong decisions of commercial managers can have undesirable consequences for their organizations. Therefore the most important issues for investors is forecasting risk in future periods. The importance of this issue was caused us to forecast Value at Risk (VaR) in one step ahead by using the exponential smoothing family for two normal and t-student distributions with confidence levels of 95%, 97.5% and also 99% in this research. Previously the classic method is commonly used to forecast future periods of VaR, but in this research the family of exponential smoothing models is used, which process data by considering trend and doing so online monitoring. In order to validate the model, the proposed model has been compared with the classic method by using backtesting. The results confirms the more accurate forecasting of proposed method in normal distribution with confidence levels of 97.5%, and 99% and also in t-student distribution with confidence levels of 97.5%, 99%. Manuscript profile