Combining Time Series Forecasting Methods for Internet Traffic

2015 ◽  
pp. 309-317 ◽  
Author(s):  
C. Katris ◽  
S. Daskalaki
Keyword(s):  
Time Series ◽  
Internet Traffic ◽  
Time Series Forecasting ◽  
Forecasting Methods

Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect

2018 ◽  
pp. 302-316 ◽  
Author(s):  
Malek Sarhani ◽  
Abdellatif El Afia
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Supply Chains ◽  
Support Vector Regression ◽  
Case Studies ◽  
Time Series Forecasting ◽  
Regression Technique ◽  
Support Vector ◽  
Reliable Prediction ◽  
Forecasting Methods

Reliable prediction of future demand is needed to better manage and optimize supply chains. However, a difficulty of forecasting demand arises due to the fact that heterogeneous factors may affect it. Analyzing such data by using classical time series forecasting methods will fail to capture such dependency of factors. This chapter addresses these problems by examining the use of feature selection in forecasting using support vector regression while eliminating the calendar effect using X13-ARIMA-SEATS. The approach is investigated in three different case studies.

scholarly journals Advanced Time Series Forecasting Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-2 ◽  
Author(s):  
Erol Egrioglu ◽  
Mehdi Khashei ◽  
Cagdas Hakan Aladag ◽  
I. Burhan Turksen ◽  
Ufuk Yolcu
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Forecasting Methods

scholarly journals ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2578 ◽  
Author(s):  
Neeraj Dhanraj Bokde ◽  
Zaher Mundher Yaseen ◽  
Gorm Bruun Andersen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Solar Radiation ◽  
Test Bench ◽  
Performance Metrics ◽  
R Package ◽  
Time Series Forecasting ◽  
Comparative Performance ◽  
Forecasting Methods ◽  
Natural Time

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods

2021 ◽  
Author(s):  
Karthikeswaren R ◽  
Kanishka Kayathwal ◽  
Gaurav Dhama ◽  
Ankur Arora
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Forecasting Methods
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