A Study of Time Series Models ARIMA and ETS

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Er. Garima Jain 1,* Bhawna Mallick 2

1. Galgotias College of Engineering and Technology/Computer Science, Gr.Noida, 201310, India

2. Galgotias College of Engineering and Technology /HOD (Computer Science), Gr.Noida, 201310, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2017.04.07

Received: 2 Dec. 2016 / Revised: 20 Jan. 2017 / Accepted: 27 Feb. 2017 / Published: 8 Apr. 2017

Index Terms

ARIMA (Autoregressive Integrated Moving Average), ETS (Exponential Smoothing), AIC (Akaike’s Information Criteria), and BIC (Bayesian Information Criteria), RMSE (Root Mean Square Error)


The aim of the study is to introduce some appropriate approaches which might help in improving the efficiency of weather’s parameters. Weather is a natural phenomenon for which forecasting is a great challenge today. Weather parameters such as Rainfall, Relative Humidity , Wind Speed , Air Temperature are highly non-linear and complex phenomena, which include statistical simulation and modeling for its correct forecasting. Weather Forecasting is used to simplify the purpose of knowledge and tools which are used for the state of atmosphere at a given place. The expectations are becoming more complicated due to changing weather state. There are different software and their types are available for Time Series forecasting. Our aim is to analyze the parameter and do the comparison of some strategies in predicting these temperatures. Here we tend to analyze the data of given parameters and to notice their predictions for a particular period by using the strategy of Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS) .The data from meteorological centers has been taken for the comparison of methods using packages such as ggplot2, forecast, time Date in R and automatic prediction strategies which are available within the package applied for modeling with ARIMA and ETS methods. On the basis of accuracy we tend to attempt the simplest methodology and then we will compare our model on the basis of MAE, MASE, MAPE and RMSE. An identification of model will be the chromatic checkup of both the ACF and PACF to hypothesize many probable models which are going to be projected by selection criteria i.e. AIC, AICc and BIC.

Cite This Paper

Er. Garima Jain, Bhawna Mallick,"A Study of Time Series Models ARIMA and ETS", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.4, pp.57-63, 2017. DOI:10.5815/ijmecs.2017.04.07


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