Exponential smoothing methods for forecasting bar diagram-valued time series

2012 ◽  
Author(s):  
C. A. G. de Araujo Junior ◽  
F. A. T. de Carvalho ◽  
Andre Luis Santiago Maia
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Smoothing Methods

scholarly journals Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 839-850
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Ufuk Yolcu
Keyword(s):  
Time Series ◽  
Search Algorithm ◽  
Harmony Search ◽  
Exponential Smoothing ◽  
Harmony Search Algorithm ◽  
Time Varying ◽  
Smoothing Methods ◽  
Forecasting Methods ◽  
Smoothing Parameters ◽  
Exponential Smoothing Method

Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. According to the time series component, a suitable exponential smoothing method should be preferred. The Holt method can produce successful forecasting results for time series that have a trend. In this study, the Holt method is modified by using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated by using a harmony search algorithm, and the forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider the time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. The real-world time series are used to show the forecasting performance of the proposed method.

scholarly journals Forecasting the spread of COVID19 in Hungary

2020 ◽  
Author(s):  
Owais Mujtaba Khanday ◽  
Samad Dadvandipour ◽  
Mohd. Aaqib Lone
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Reproduction Number ◽  
Sir Model ◽  
Exponential Smoothing ◽  
Short Term ◽  
Smoothing Methods ◽  
Series Analysis ◽  
Short Term Forecasting

AbstractTime series analysis of the COVID19/ SARS-CoV-2 spread in Hungary is presented. Different methods effective for short-term forecasting are applied to the dataset, and predictions are made for the next 20 days. Autoregression and other exponential smoothing methods are applied to the dataset. SIR model is used and predicted 64% of the population could be infected by the virus considering the whole population is susceptible to be infectious Autoregression, and exponential smoothing methods indicated there would be more than a 60% increase in the cases in the coming 20 days. The doubling of the number of total cases is found to around 16 days using an effective reproduction number.

Validation en temps réel des données des apports naturels journaliers pour la gestion des réservoirs

1998 ◽  
Vol 25 (6) ◽  
pp. 1096-1102
Author(s):  
Van-Thanh-Van Nguyen ◽  
Jean-Louis Bisson
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Estimation Method ◽  
Exponential Smoothing ◽  
Numerical Application ◽  
Smoothing Methods ◽  
Simple Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method ◽  
Simple Parameter

The main objective of this study is to propose a method for validating the daily net basin supply data to obtain the most reliable values for the management of water resource systems. Based on the regression and exponential smoothing methods, various validating procedures are developed and compared. Results of a numerical application using data from La Grande 3 basin have shown that the simple exponential smoothing method is the most suitable. Further, it has been found that the proposed exponential smoothing has the simplest structure and would require a simple parameter estimation method because it contains only one parameter.Key words: real time validation, net basin supply, forecasting, time series analysis, reservoir management.

LINEAR DECAYING WEIGHTS FOR TIME SERIES SMOOTHING: AN ANALYSIS

2014 ◽  
Vol 22 (01) ◽  
pp. 23-40 ◽  
Author(s):  
R. BALLINI ◽  
R. R. YAGER
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Small Variance ◽  
Smoothing Methods ◽  
New Class

In this paper, we investigate the use of weighted averaging aggregation operators as techniques for time series smoothing. We analyze the moving average, exponential smoothing methods, and a new class of smoothing operators based on linearly decaying weights from the perspective of ordered weights averaging to estimate a constant model. We examine two important features associated with the smoothing processes: the average age of the data and the expected variance, both defined in terms of the associated weights. We show that there exists a fundamental conflict between keeping the variance small while using the freshest data. We illustrate the flexibility of the smoothing methods with real datasets; that is, we evaluate the aggregation operators with respect to their minimal attainable variance versus average age. We also examine the efficiency of the smoothed models in time series smoothing, considering real datasets. Good smoothing generally depends upon the underlying method's ability to select appropriate weights to satisfy the criteria of both small variance and recent data.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

The predicted load balancing algorithm based on the dynamic exponential smoothing

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 439-447
Author(s):  
Lijie Yan ◽  
Xudong Liu
Keyword(s):  
Time Series ◽  
Load Balancing ◽  
Exponential Smoothing ◽  
Load Prediction ◽  
The Common ◽  
Load Balancing Algorithm ◽  
Balancing Efficiency

AbstractTo a large extent, the load balancing algorithm affects the clustering performance of the computer. This paper illustrated the common load balancing algorithms and elaborated on the advantages and drawbacks of such algorithms. In addition, this paper provides a kind of balancing algorithm generated on the basis of the load prediction. Due to the dynamic exponential smoothing model, such an algorithm helps obtain the corresponding smoothing coefficient with the server node load time series of current phrase and allows researchers to make prediction with the load value at the next moment of this node. Subsequently, the dispatcher makes the scheduling with the serve request of users according to the load predicted value. OPNET Internet simulated software is applied to the test, and we may conclude from the results that the application of such an algorithm acquires a higher load balancing efficiency and better load balancing effect.

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