NDLI: An Automatic Forecasting Method for Time Series

Content Provider	IET Digital Library
Author	Liu, Shufen Gu, Songyuan Bao, Tie
Abstract	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.
Starting Page	445
Ending Page	452
Page Count	8
ISSN	10224653
Volume Number	26
e-ISSN	20755597
Issue Number	Issue 3, May (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/26/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2017.01.011
Journal	Chinese Journal of Electronics
Publisher Date	2017-05-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	ARIMA Model Automatic Forecasting Method Autoregressive Integrated Moving Average Model Autoregressive Moving Average Processes Data Driven Forecasting Algorithm Econometric Theory Econometrics Economic Forecasting Holt Exponential Smoothing Method Linear Time Series Forecasting Modeling Stage Preprocessing Stage Stationary Processing Stage Statistics System Theory Application in Economics And Business Time Series Unit Root Testing Stage
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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