Comparative Analysis of ANFIS, ARIMA and Polynomial Curve Fitting for Weather Forecasting

Srikanth, P. L.; Rao, D. Srinivasa; Pellakuri, Vidyullatha

doi:10.17485/ijst/2016/v9i15/89814

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…In this context, Kafazi et al [16] used the curve-fitting technique for energy forecasting, whereas Donmez et al [17] used a similar approach to forecast the electricity demand. In another work, Srikanth et al [18] compared the performance of polynomial curve-fitting, ARIMA, and ANFIS methods and concluded that the polynomial curve outperformed the other two methods. Jalil et al [19] employed the polynomial and Gaussian fits to develop a forecast model of solar radiation.…”

Section: Curve Fittingmentioning

confidence: 99%

Active Learning-Based Estimation of COVID-19 Pandemic: A Synergetic Case Study in Selective Regions Population

Chakraborty¹,

Mitra²,

Das³

et al. 2021

Healthcare Informatics for Fighting COVID-19 and Future Epidemics

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Section: Curve Fittingmentioning

confidence: 99%

Active Learning-Based Estimation of COVID-19 Pandemic: A Synergetic Case Study in Selective Regions Population

Chakraborty¹,

Mitra²,

Das³

et al. 2021

Healthcare Informatics for Fighting COVID-19 and Future Epidemics

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“…The ARIMA model was initially experimented by Box and Jenkins and ARIMA models, so they sometimes mentioned as Box-Jenkins models. [6] [7]. According to Box and Jenkins(1976), the arima modelling fallows the three main stages they are 1.…”

Section: Arima Modelmentioning

confidence: 99%

Explore Big Data and Forecasting Future Values using Univariate Arima Model in R

Imambi¹,

Pellakuri²,

V.B.T.Santhi³

et al. 2018

IJET

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Electronic equipment and sensors spontaneously create diagnostic data that needs to be stocked and processed in real time. It is not only difficult to keep up with huge amount of data but also reasonably more challenging to analyze it. Big Data is providing many opportunities for organizations to evolve their processes they try to move beyond regular BI activities like using data to populate reports. Predicting future values is one of the requirements for any business organization. The experimental results shows that time series model with ARIMA (3,0,1)(1,0,0) is best fitted for predicting future values of the sales.

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“…The regression techniques employed for prediction were support vector regression (SVR), Random Forest (RF), and Decision Tree (DT), demonstrating that Random Forest is the best regression strategy for rainfall prediction (RF) (Tharun et al, 2018). A comparison of ANFIS, ARIMA, and the suggested Fuzzy based Curve tting for weather forecasting was conducted using SSE, R2, RMSE, and MAE, and it was discovered that the curve tting based on fuzzy logic outperforms ANFIS and ARIMA (Srikanth et al, 2016). Statistical downscaling local polynomial regression was used to derive future rainfall estimates in the catchment of the Idukky reservoir in Kerala, India .…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of ARIMA and other Statistical Techniques in Rainfall Forecasting: A Case Study in Kolkata (KMC), West Bengal

Alam

Majumder

2022

Preprint

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Rainfall forecasting in urban environments is an important concern for city planners since it is linked to urban water management. In this study, the ARIMA (auto-regressive integrated moving average) model, as well as several regression approaches such as simple linear and second to sixth-degree polynomial regression equations have been used to forecast the annual rainfall based on 120 years of monthly and annual rainfall from 1901 to 2020 in Kolkata Municipal Corporation (KMC), West Bengal. It is a comparison of ARIMA and other regression techniques for forecasting rainfall using r squared and root mean square error (RMSE). The ARIMA model has been implemented through machine language using the python platform and other regression equations have been computed and analyzed in Microsoft Excel 2019. For using ARIMA, all assumptions were tested and the best order of the model was determined using the import auto-Arima package from the pmdarima.arima library and the stepwise model.aic function, which produced 0,1,1 as the best order of the model. The result shows that among all the regression approaches used to forecast rainfall the fifth-degree polynomial equation has the lowest RMSE making it the best model to forecast rainfall in this analysis.

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Comparative Analysis of ANFIS, ARIMA and Polynomial Curve Fitting for Weather Forecasting

Cited by 7 publications

References 5 publications

Active Learning-Based Estimation of COVID-19 Pandemic: A Synergetic Case Study in Selective Regions Population

Active Learning-Based Estimation of COVID-19 Pandemic: A Synergetic Case Study in Selective Regions Population

Explore Big Data and Forecasting Future Values using Univariate Arima Model in R

A Comparative Analysis of ARIMA and other Statistical Techniques in Rainfall Forecasting: A Case Study in Kolkata (KMC), West Bengal

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