Holt's Exponential Smoothing Method Research Articles

Amount of Poverty as Policy Basis: A Forecasting Using The Holt Method

This research aims to predict the growth of the number of poor people in every district and city in NTB Province of Indonesia for the next 10 years by using Holt exponential smoothing method. This type of research is quantitative research with input data used over the last 19 years with a measure of the goodness of models namely MSE, MAD, and MAPE. Based on the optimization results obtained the smallest parameter at α of 0.9 and β of 0.1, an average value MSE of 278005053.7, MAD of 9992.28222, and MAPE of 8.9374 Optimization results also provide information that the increase in the growth of the average poor population of 70206.6604 Certainly this result can be used as a guideline by the government in determining steps and responding to rapid response strategies in dealing with this poverty problem.

An Automatic Forecasting Method for Time Series

An automatic forecasting method is proposed concerning automation problem in the field of linear time series forecasting. The method is on the basis of econometric theory and overcomes the difficulty to mine and forecast automatically with econometric models. The proposed algorithm is divided into 4 stages, i.e. preprocessing, unit root testing and stationary processing, modeling, and ultimately forecasting. Future values and trends would be estimated and forecasted precisely through the 4 stages of the algorithm according to input data without manual intervention. Experimental comparisons were made between the proposed algorithm and the 2 data driven forecasting algorithms, i.e. moving average method and Holt exponential smoothing method. It was demonstrated with the experimental results that automatic forecasting is feasible utilizing the proposed algorithm and higher accuracy can be acquired than these 2 data driven-based methods.

Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model

In view of the importance of interval-valued time series (ITS) modeling and forecasting, and the less research efforts made before, this study proposes an hybrid modeling framework combining interval Holt's exponential smoothing method (HoltI) and multi-output support vector regression (MSVR) for ITS forecasting. Following the philosophy of well-established hybrid “linear and nonlinear” modeling framework, HoltI and MSVR are committed to capture the linear and nonlinear patterns hidden in ITS, respectively. Different from the previous studies considering to model the highs and lows of intervals separately, the proposed hybrid method (termed as HoltI-MSVR) is used to model and forecast the daily highs and lows of ITS simultaneously, taking into account the possible interrelations between the bounds. Three ITS datasets extracted from finance market and energy market are used to compare the prediction performance of the HoltI-MSVR with five selected competitors. The experimental results are judged on the basis of statistical criteria, i.e., the goodness of forecast measure and the accuracy compared to competing forecasts test, and economic criteria, i.e., the returns obtained from a simple trading strategy based on the interval forecasts. The results obtained suggest that the proposed HoltI-MSVR is a promising alternative for ITS forecasting.

Holt's exponential smoothing method for forecasting silver prices

In this paper, it is suggested that for forecasting the silver prices, the optimum technique is the Holt's exponential smoothing method. For getting this conclusion we have collected the data on average monthly silver prices for period of 7 years from Jan 2008 to Dec 2014 obtained from World Silver Council and applied Single Exponential Smoothing Method, Holt's Exponential Smoothing Method and Auto Regressive Moving Average (ARMA) Methods. By comparing the above techniques basing on measures of accuracy say, Mean Absolute Percent Error(MAPE) measure, it was found that MAPE is minimum for Holt's method. Using this method to forecast the silver prices for the succeeding period.

Forecasting gold prices using holt's exponential smoothing method

Forecasting is an important part of decision making and many of our decisions are based on estimation of future unknown events. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as a money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential currency. The demand for this commodity is on the rise.