scholarly journals Scale Analysis and Correlation Study of Wildfire and the Meteorological Factors That Influence It

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jiazheng Lu ◽  
Tejun Zhou ◽  
Bo Li ◽  
Chuanping Wu
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Daily Precipitation ◽  
Meteorological Factors ◽  
Fluctuation Analysis ◽  
Average Temperature ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

Wildfire is a large-scale complex system. Insight into the mechanism that drives wildfires can be revealed by the distribution of the wildfire over a large time scale, which is one of the important topics in wildfire research. In this study, the scaling properties of four meteorological factors (relative humidity, daily precipitation, daily average temperature, and maximum wind speed) that can affect wildfires (number of wildfires per day) were investigated by using the detrended fluctuation analysis method. The results showed that the time series for these meteorological factors and wildfires have similar power exponents and turning points for the power exponents curve. The five types of time series have a lasting and steady long-range power law correlation over a certain time scale range, where the corresponding exponents were 0.6484, 0.5724, 0.8647, 0.7344, and 0.6734, respectively. They also have a reversible long-range power law correlation beyond a certain time scale, where the corresponding exponents are 0.3862, 0.2218, 0.1372, 0.2621, and 0.2678. The multifractal detrended fluctuation analysis results showed that the wildfire time series were multifractal. The results of the research based on the detrended cross-correlation analysis and the multifractal detrended cross-correlation analysis showed that relative humidity and daily precipitation have a considerable impact on the wildfire time series, while the impacts of daily average temperature and the maximum wind speed are relatively small. This study showed that identifying the factors causing the inherent volatility in the wildfire time series can improve understanding of the dynamic mechanism controlling wildfires and the meteorological parameters. These results can also be used to quantify the correlation between wildfire and the meteorological factors investigated in this study.

scholarly journals CORRELAÇÃO DE LONGO ALCANCE TEMPORAL DA VELOCIDADE DO VENTO NOS MUNICÍPIOS DE CEARÁ-MIRIM E NATAL NO RIO GRANDE DO NORTE

2017 ◽  
Vol 8 ◽  
pp. 56 ◽  
Author(s):  
Neilson Ferreira de Lima ◽  
Marcos Antônio Chaves Freire ◽  
Josimar José dos Santos ◽  
Rodrigo Ricardo Cavalcante de Albuquerque
Keyword(s):  
Correlation Analysis ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Rio Grande ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

A energia eólica é uma fonte natural de energia renovável e utilizada em diversos países para o abastecimento energético de residências, fábricas e empresas. Para os países que possuem hidrelétricas como a principal fonte geradora de energia, como o Brasil, por exemplo, a energia eólica é muito importante, porque ela não consome água, é renovável, limpa e não causa danos ambientais como outras fontes energéticas poluentes e sujas. Diversos estudos são realizados a fim de observar o comportamento do vento, em particular às correlações com outras variáveis como radiação solar, temperatura máxima ou mínima e umidade relativa do ar. Para fazerem inferência das observações do vento pesquisadores tem empregado diversas ferramentas estatísticas como médias móveis, média móvel ponderada e suavização exponencial. Nosso interesse é verificar as correlações de curto ou longo alcance persistente/antipersistente em séries temporais de ventos dos municípios Natal e Ceará-Mirim. Para realizar o estudo da correlação do vento se aplicou os métodos estatísticos denominados Detrended Fluctuation Analysis (DFA) e Detrended Cross-Correlation Analysis –DCCA, isto é análise da flutuação sem tendências e análise da correlação cruzada sem tendências. Nesta pesquisa observou-se que a série temporal do vento tem uma forte correlação de longo alcance persistente, significando que valores altos de velocidade do vento tem maior probabilidade de ser seguido por valores altos; e, valores baixos na velocidade do vento tem maior probabilidade de ser seguido por valores baixos.

scholarly journals The use of a Detrended Cross-Correlation Analysis on returns from agricultural commodities in the subprime crisis

2021 ◽  
Vol 16 (03) ◽  
pp. 119-137
Author(s):  
Luiza Lonardoni Paulino Schiavon ◽  
Antônio Fernando Crepaldi
Keyword(s):  
Financial Crisis ◽  
Correlation Analysis ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Fluctuation Analysis ◽  
Subprime Crisis ◽  
Product Portfolio ◽  
Agricultural Commodities ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

Purpose: To understand the dynamics of the agricultural commodities market and predict a possible economic crisis, in addition to helping agricultural producers balance their product portfolio, diversifying their goods and reducing risks. Theoretical framework: Prices of agricultural commodities have changed significantly since 2002; although had been an increase in demand, where weather problems negatively affected supply, resulting in price increases. With the global financial crisis of 2008, there was a reduction in international credit and an increase in the US dollar against the Brazilian Real. Design/Methodology/Approach: Detrended Cross-Correlation Analysis and Detrended Fluctuation Analysis methods were used to understand the behavior of the cross correlations of the price of five Brazilian agribusiness commodities (cotton, sugar, coffee, corn and soybeans) for the previous periods, during and after the subprime crisis. Findings: Both methods showed a significant change in the behavior of the series in the period of crisis, when compared to their temporal neighborhoods. Research, Practical & Social Implications: It was found that the crisis changed the structure of the correlation of the returns on the commodities analyzed. This change implies alterations to a possible product portfolio in order to minimize risks. Originality/Value: The long-term nonlinear correlation behavior was calculated and analyzed on the temporal series for the return on the main agricultural commodities in the period of the subprime crisis and its temporal neighborhoods were calculated and analyzed, allowing several changes to be found in the product correlation structure, due to the crisis process. Keywords: Subprime Financial Crisis; Agricultural Commodities; Detrended Fluctuation Analysis; Detrended Cross-Correlation Analysis.

scholarly journals Correlações em séries temporais de preços de manga produzida no Vale do São Francisco

2021 ◽  
Vol 10 (12) ◽  
pp. e266101220460
Author(s):  
Bruno de Freitas Assunção ◽  
Ikaro Daniel de Carvalho Barreto ◽  
Tatijana Stosic ◽  
Borko Stosic
Keyword(s):  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

Nas últimas décadas o comércio de frutas no Brasil cresceu expressivamente atendendo ao mercado interno e externo. Dentre as principais frutas produzidas e comercializadas, destaca-se a manga, a fruta mais exportada pelo Brasil. Neste trabalho foram analisadas as séries temporais de retornos e de volatilidade de preços semanais de duas variedades de manga,  Palmer e Tommy Atkins, produzidas no Vale do São Francisco, a região com a maior produção de manga no país. Foram utilizados os métodos Detrended Fluctuation Analysis (DFA) e Detrended Cross-Correlation Analysis (DCCA) para calcular expoentes de escala de autocorrelações e correlações cruzadas entre as séries analisadas. Os resultados mostraram que as séries de volatilidade apresentam persistência mais forte do que as séries de retornos que apresentaram dois regimes de invariância de escala com correlações antipersistentes nas escalas maiores. As correlações cruzadas entre as séries de retornos também apresentaram dois regimes de escala com expoentes semelhantes as séries de retornos da variedade Tommy. Os valores do coeficiente de correlação obtidos pelo método Detrended Cross Correlation Coefficient mostraram que para ambas, retornos e volatilidade, as correlações entre as séries são positivas, aumentam com escala temporal e são mais fortes para as séries de retornos.

MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS BASED ON PSEUDO-BILINEAR FRACTAL INTERPOLATION FUNCTIONS ON METALLIC GLASSES

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850047
Author(s):  
QINGGE KONG ◽  
QING YU ◽  
MEIFENG DAI ◽  
YUE ZONG ◽  
XIAODONG WANG ◽  
...  
Keyword(s):  
Metallic Glasses ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Fluctuation Analysis ◽  
Fractal Interpolation ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Fractal Interpolation Functions ◽  
Detrended Fluctuation ◽  
Interpolation Functions

Based on the multifractal detrended cross-correlation analysis, which is the most effective way to detect long-range cross-correlation of time series, in this paper, we present a new method called multifractal detrended fluctuation analysis based on pseudo-bilinear fractal interpolation functions (MFDFA-PBFIF). In order to get a better detrended effect, we replace the polynomial fitting with PBFIFs in detrended process, and the result shows that the MFDFA-PBFIF can achieve a more accurate result. Then, we analyze the Legendre spectrum to detect the multifractal property on metallic glasses with MFDFA-PBFIF.

scholarly journals Correlações em séries temporais de preços de frango, soja e milho

2021 ◽  
Vol 10 (4) ◽  
pp. e20610414019
Author(s):  
Ruben Vivaldi Silva Pessoa ◽  
Ikaro Daniel de Carvalho Barreto ◽  
Lidiane da Silva Araújo ◽  
Guilherme Rocha Moreira ◽  
Tatijana Stosic ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Sao Paulo ◽  
Fluctuation Analysis ◽  
São Paulo ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

A evolução do mercado agrícola brasileiro alterou o processo de produção, exportação e consumo de commodities alimentares. Com isso, novos estudos acerca da relação entre o mercado de alimentos e outros mercados foram desenvolvidos, buscando explicar a ligação entre os preços de commodities agrícolas e não agrícolas. Visando contribuir para esse estudo, foram investigadas as correlações de longo prazo entre os preços de commodities agrícolas brasileiras, utilizando técnicas de Econofísica. Analisaram-se então as séries diárias de preços e retorno de preços da carne de frango, soja e milho, registrados entre 02/08/2004 e 16/06/2017 pelo Centro de Estudos Avançados em Economia Aplicada / Escola Superior de Agricultura Luiz de Queiroz / Universidade de São Paulo - CEPEA/ESALQ/USP. As correlações entre as séries temporais foram investigadas utilizando os métodos Detrended Fluctuation Analysis (DFA) e Detrended Cross Correlation Analysis (DCCA), para calcular o Detrended Cross Correlation Coefficient (DCCA Coefficient), que serve para quantificar correlações de longo prazo entre séries temporais não estacionárias. Os resultados obtidos apontam para a ausência de correlações nas escalas de até 30 dias e, para escalas maiores, acusam correlações mais fortes entre os preços de frango e milho que entre os preços de frango e soja. Após a crise alimentar de 2008, entretanto, as correlações entre as séries diárias de retorno de preços do frango e do milho diminuíram, enquanto que, entre as de frango e soja, aumentaram nas escalas menores e diminuíram nas escalas maiores.

scholarly journals AUTOMATICALLY DETECTING "SIGNIFICANT EVENTS" ON SenseCam

2013 ◽  
Vol 11 (06) ◽  
pp. 1350050 ◽  
Author(s):  
NA LI ◽  
MARTIN CRANE ◽  
HEATHER J. RUSKIN
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Time Windows ◽  
Fluctuation Analysis ◽  
Discrete Wavelet ◽  
Dominant Eigenvalue ◽  
Multiple Time ◽  
Correlation Relationship ◽  
Significant Events ◽  
Detrended Fluctuation

SenseCam is an effective memory-aid device that can automatically record images and other data from the wearer's whole day. The main issue is that, while SenseCam produces a sizeable collection of images over the time period, the vast quantity of captured data contains a large percentage of routine events, which are of little interest to review. In this article, the aim is to detect "Significant Events" for the wearers. We use several time series analysis methods such as Detrended Fluctuation Analysis (DFA), Eigenvalue dynamics and Wavelet Correlations to analyse the multiple time series generated by the SenseCam. We show that Detrended Fluctuation Analysis exposes a strong long-range correlation relationship in SenseCam collections. Maximum Overlap Discrete Wavelet Transform (MODWT) was used to calculate equal-time Correlation Matrices over different time scales and then explore the granularity of the largest eigenvalue and changes of the ratio of the sub-dominant eigenvalue spectrum dynamics over sliding time windows. By examination of the eigenspectrum, we show that these approaches enable detection of major events in the time SenseCam recording, with MODWT also providing useful insight on details of major events. We suggest that some wavelet scales (e.g., 8 minutes–16 minutes) have the potential to identify distinct events or activities.

scholarly journals Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1157
Author(s):  
Faheem Aslam ◽  
Saima Latif ◽  
Paulo Ferreira
Keyword(s):  
Time Series ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Financial Time Series ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Detrended Fluctuation

The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

scholarly journals Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

2019 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Natália Costa ◽  
César Silva ◽  
Paulo Ferreira
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Financial Assets ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Short Time ◽  
Detrended Fluctuation ◽  
Great Development

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

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