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2012•IEEE-International Conference On Advances In Engineering, Science And ManagementRequires access

Predictive data mining on Average Global Temperature using variants of ARIMA models

C. Narendra Babu, B. Eswara Reddy

Open publisher page 37 citations

Abstract

This paper analyzes and predicts the Average Global Temperature time series data. Three different variants of ARIMA models: Basic ARIMA, Trend based ARIMA and Wavelet based ARIMA have been used to predict the average global temperature. Out of all the three linear models, it has been observed that Trend based ARIMA method outperforms basic ARIMA method and Wavelet based ARIMA method outperforms Trend based ARIMA method. MAPE (Mean Absolute Percentage Error), MaxAPE (Maximum Absolute Percentage Error) and MAE (Mean Absolute Error) have been used as a performance measures to compare between the models.

About this research paper

What this paper is about

This paper analyzes and predicts the Average Global Temperature time series data. Three different variants of ARIMA models: Basic ARIMA, Trend based ARIMA and Wavelet based ARIMA have been used to predict the average global temperature. Out of all the three linear models, it has been observed that Trend based ARIMA method outperforms basic ARIMA method and Wavelet based ARIMA method outperforms Trend based ARIMA method. MAPE (Mean Absolute Percentage Error), MaxAPE (Maximum Absolute Percentage Error) and MAE (Mean Absolute Error) have been used as a performance measures to compare between the models.

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OpenAlex reports 37 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper analyzes and predicts the Average Global Temperature time series data. Three different variants of ARIMA models: Basic ARIMA, Trend based ARIMA and Wavelet based ARIMA have been used to predict the average global temperature. Out of all the three linear models, it has been observed that Trend based ARIMA method outperforms basic ARIMA method and Wavelet based ARIMA method outperforms Trend based ARIMA method. MAPE (Mean Absolute Percentage Error), MaxAPE (Maximum Absolute Percentage Error) and MAE (Mean Absolute Error) have been used as a performance measures to compare between the models.

Key concepts: Autoregressive integrated moving average, Mean absolute percentage error, Statistics, Mean absolute error, Time series, Moving average, Series (stratigraphy), Approximation error

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