Forecasting Daily Maximum Temperature of Chennai using Nonlinear Prediction Approach

Indira, P.; Inbanathan, S.S.R.; Rajalakshmi, S.; Suresh, Aksheya

doi:10.17485/ijst/2016/v9i39/100776

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…First parthelp to determine whether the observe time series is either random or chaos while the later part help in predictingfuture temperature time series. By using the reconstructed phase space, through the method such as Lyapunov exponent, phase space approach and Poincare map research by [23], [9]as well as [24]resulted that the nature of their observed temperature time series is chaos. Phase space approach is quite simple while Cao method [25]can helpindifferentiatingthe time series' nature.…”

Section: Methodsmentioning

confidence: 99%

A Pilot Study Using Chaos Theory to Predict Temperature Time Series in Malaysian Semi Urban Area

et.al¹

2021

TURCOMAT

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Chaos theory draws more attention since it has been widely used in modeling various time series. Changes in temperature can cause bad effect on health and lead to death. Therefore, in this study, chaos theory was applied to the temperature time series. The temperature time series is observed hourly in one of Malaysiansemi urban area namely Tanjong Malim,located in the state of Perak. This pilot study begins by detecting the chaos nature in time series through phase space approach and Cao method. Next, the time series was predicted through the local approximation method, a method based on chaos theory. This study resulted that the nature of the observed temperature time series was chaos. Prediction through the local approximation method was success with correlation coefficient value 0.9138. This shows that there exist a strong relationship between the predicted and observed temperature time series. Therefore, chaos theorywas a good approach that can be used to determine the nature and predict temperature time series in the semi urban area. In implication, this findingwas expected to serve stakeholders such as Ministry of Higher Education, Meteorological Department as well as Department of Environment in temperature time series management.

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Section: Methodsmentioning

confidence: 99%

A Pilot Study Using Chaos Theory to Predict Temperature Time Series in Malaysian Semi Urban Area

et.al¹

2021

TURCOMAT

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“…Pendekatan kalut ini telah diaplikasikan dalam pelbagai bidang secara meluas termasuk di dalam dan di luar negara. Sebagai contoh di luar negara, pendekatan kalut ini telah digunakan dalam peramalan siri masa O3 (Indira et al, 2016) dan juga siri masa gelombang angin (Kermani & Kisi, 2015). Manakala, kajian di dalam negara pula, pendekatan kalut ini telah diaplikasi oleh Hamid (2012) bagi meramal data hujan harian, peramalan arus sungai oleh Mashuri et al (2019) dan juga peramalan siri masa NO2 oleh Jusoh dan Hamid (2020).…”

Section: Pengenalanunclassified

Forecasting Carbon Monoxide (CO) Pollutant in Putrajaya using the Local Mean Approximation Method

Shahizam,

Abd Hamid,

Side

2021

JSML

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Air polution is a great concern to all parties because it is not only affects human health but also affects the environment and the development of the country. This study examines the forecast of carbon monoxide (CO) in metropolitan areas in Malaysia using the local mean approximation method. This study aims to predict the concentration of CO pollutant that has been observed hourly at the selected area which is Putrajaya. The delay time parameter was obtained from the determination method while embedding dimension was obtained from the Cao method. The presence of chaotic dynamics is successfully detected by using the Cao method and the phase space plot. The one-hour forward forecasting process of CO time series using and was carried out using the Local Mean Approximation Method. The result showed that correlation coefficient value is 0.7674 which is close to 1. This forecasting is expected to assist those stakeholders in managing CO pollution effectively especially in metropolitan areas in Malaysia.

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“…Lorenz in 1963 conducted a study on the convective model of the atmosphere, establishing climate as a chaotic system (3) . Various studies have analyzed chaotic systems through nonlinear differential equations (3) , logistic maps (4) , and chaotic time series analysis (5)(6)(7)(8) . Examples include the work of P. Indira, S.S.R Ibanathan, R.S.…”

Section: Introductionmentioning

confidence: 99%

“…Examples include the work of P. Indira, S.S.R Ibanathan, R.S. Selvaraj, and A.A. Suresh (5) on daily maximum temperature in Chennai, India, and A.T. Adewole, E.O. Falayi, T.O.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Approach for Calculating Minimum Embedding Dimension

Shandilya,

Kumar,

Singh

2024

IJST

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Objective:The aim of this study is to apply different methods to identify chaos in the temperature time series data of Delhi, India, spanning from 1995 to 2019 during the dry season. The goal is to assess the minimum embedding dimension and, consequently, determine the minimum number of factors influencing the temperature time series data. Methods: Lyapunov exponent and correlation dimension calculations have been employed to illustrate the chaotic nature of the data. A plot depicting E2(m) against m has been generated to differentiate data behaviour from stochastic to chaotic. To establish the minimum embedded dimension, an appropriate time lag (τ) has been computed from the plot of Actual Mutual Information (AMI) versus time lag. The Cao method has been utilized to ascertain the minimum embedded dimension. Findings: The Lyapunov exponent value is found to be 0.01900, and the correlation dimension value is 0.90860. The positive Lyapunov exponent value and the non-integral correlation dimension value serve as evidence for the existence of chaos in the data. The minimum AMI value occurs at a time lag of 2 days. Utilizing this minimum AMI value and the Cao method, the minimum embedding dimension is determined to be 7. Therefore, the minimum number of parameters influencing the temperature is identified as 7. Novelty: Unlike many studies that solely rely on the Lyapunov exponent to detect chaos in time series data and use the reconstruction space method to determine embedding dimension, this study incorporates the Cao method for chaotic analysis of time series data. In addition to Lyapunov exponent and correlation dimension, the Cao method is employed to analyse chaotic behaviour and calculate the minimum embedding dimension, providing a comparative analysis between the two methods.

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Forecasting Daily Maximum Temperature of Chennai using Nonlinear Prediction Approach

Cited by 5 publications

References 10 publications

A Pilot Study Using Chaos Theory to Predict Temperature Time Series in Malaysian Semi Urban Area

A Pilot Study Using Chaos Theory to Predict Temperature Time Series in Malaysian Semi Urban Area

Forecasting Carbon Monoxide (CO) Pollutant in Putrajaya using the Local Mean Approximation Method

A Chaotic Approach for Calculating Minimum Embedding Dimension

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