A comparative study between conventional ARMA and Fourier ARMA in modeling and forecasting wind speed data

Jamaludin, Aaishah Radziah; Yusof, Fadhilah; Kane, Ibrahim Lawal; Norrulasikin, Siti Mariam

doi:10.1063/1.4954627

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…The monthly, annual and seasonal maps were derived using geographic information system (GIS) spatial interpolation, for the land area over Nigeria. Kriging spatial interpolation model was used to analyse the aerosol seasonal distribution (Ayanlade, 2009; Jamaludin et al, 2016). Kriging uses a weighting mechanism that assigns more influence to the nearer data points to interpolate values at unknown locations.…”

Section: Methodsmentioning

confidence: 99%

Spatial and seasonal variations in atmospheric aerosols over Nigeria: Assessment of influence of intertropical discontinuity movement

Ayanlade

Atai

Jegede

2019

Journal of Ocean and Climate: Science, Technology and Impacts

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This study examines the influence of intertropical discontinuity movement on seasonality and distribution of atmospheric aerosols over Nigeria, using remote sensing approach. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra aerosol optical depth, wind speed and precipitation/intertropical discontinuity (ITD) dataset were used. Geospatial interpolation model was used to analyse the aerosol seasonal distribution. Correlations analysis was used to evaluate the degree of influence of wind and ITD on the monthly distribution of aerosol. The results show significant variations in monthly mean distributions of aerosol, but the variation is much more extraordinary during Harmattan season than Wet and Dry seasons, with 0.29 ⩽ aerosol optical depth ⩾ 0.46. In other ecological zones, the highest mean aerosol optical depth values were observed in the months of December, January and February with 0.30 ⩽ aerosol optical depth ⩾ 0.60, with highest value in Sahel ecological zone. Generally, the results further show a strong relationship between aerosol optical depth distribution and migration of ITD with correlation r 2 ⩾ 0.60 @ p = 0.05 mostly during Dry and Harmattan seasons but relatively low correlation r 2 ⩽ 0.40 @ p = 0.05 during Wet season. The major findings of this study are that seasonal shifts in the location of the ITD considerably affect not only rainfall distribution, resulting in the Wet and Dry seasons in the study area, but also have significant impacts on atmospheric aerosol distributions. Although not all aerosols presented in this study are dust originated from Sahara desert, since biomass-burning activities frequently occur in the study area, the study concludes that satellite-based aerosol optical depth datasets continue to be advantageous to understand atmospheric aerosols distribution in a region where there is fewer ground aerosols data.

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Section: Methodsmentioning

confidence: 99%

Spatial and seasonal variations in atmospheric aerosols over Nigeria: Assessment of influence of intertropical discontinuity movement

Ayanlade

Atai

Jegede

2019

Journal of Ocean and Climate: Science, Technology and Impacts

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“…A statistical inference of time series analysis will be affected by the presence of serial correlation. A fitted model is appropriate or accurate if the residuals has the conditions of zero mean, homoscedastic, independent, and normally distributed (Jamaludin et al, 2016;Yürekli et al, 2005). One of the very useful diagnostic tools to measure the existence of a serial autocorrelation for residuals in the stationary ARIMA model is using the Ljung-Box (LB) test (Kim et al, 2004).…”

Section: Serial Correlationmentioning

confidence: 99%

“…This shows that the ARIMA-GARCH model has precisely captured the dynamics in the wind speed daily series. However, further investigation should be done to treat the presence of serial autocorrelation in time series data collected from the stations NS4, ES7, ES8, and SS14, and the presence of ARCH effect in time series data collected from the stations NS1 and ES8 using other type of GARCH family models since it has proven to be very successful in describing the volatility dynamics in a short period of time (Jamaludin et al, 2016).…”

Section: Tablementioning

confidence: 99%

Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models

Hussin¹,

Yusof²,

Jamaludin³

et al. 2021

JST

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In the global energy context, renewable energy sources such as wind is considered as a credible candidate for meeting new energy demands and partly substituting fossil fuels. Modelling and forecasting wind speed are noteworthy to predict the potential location for wind power generation. An accurate forecasting of wind speed will improve the value of renewable energy by enhancing the reliability of this natural resource. In this paper, the wind speed data from year 1990 to 2014 in 18 meteorological stations throughout Peninsular Malaysia were modelled using the Autoregressive Integrated Moving Average (ARIMA) to forecast future wind speed series. The Ljung-Box test was used to determine the presence of serial autocorrelation, while the Engle’s Lagrange Multiplier (LM) test was used to investigate the presence of Autoregressive Conditional Heteroscedasticity (ARCH) effect in the residual of the ARIMA model. In this study, three stations showed good fit using the ARIMA modelling since no serial correlation and ARCH effect were present in the residuals of the ARIMA model, while the ARIMA-GARCH had proven to precisely capture the nonlinear characteristic of the wind speed daily series for the remaining stations. The forecasting accuracy measure used was based on the value of root mean square error (RMSE) and mean absolute percentage error (MAPE). Both ARIMA and ARIMA-GARCH model proposed provided good forecast accuracy measure of wind speed series in Peninsular Malaysia. These results will help in providing a quantitative measure of wind energy available in the potential location for renewable energy conversion.

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“…We first fit simple ARIMA models over the series, by not treating the observed autocorrelations as indicators of a seasonal pattern, but rather as noise. Second, we adopted two different approaches for dealing with possible periodic effects: we fitted ARIMA models over the seasonally adjusted processes (Davey & Flores, 1993 ; Giacalone et al, 2020 ) and we ran regressions with ARIMA errors by adding Fourier terms as external regressors (Jamaludin et al, 2016 ; Jebb et al, 2015 ; Pankratz, 1991 ). With respect to the first of these techniques, deseasonalizing was achieved through the function “seasadj” in R (Hyndman & Khandakar, 2008 ), which subtracts the seasonal patterns estimated through the MSTS algorithm from the observations.…”

Section: Methodsmentioning

confidence: 99%

Spontaneous charitable donations in Sweden before and after COVID: A natural experiment

Erlandsson

Nilsson

Alì

et al. 2022

Journal of Philanthropy and Marketing

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Did the outbreak of COVID‐19 influence spontaneous donation behavior? To investigate this, we conducted a natural experiment on real donation data. We analyzed the absolute amount, and the proportion of total payments, donated by individuals to charitable organizations via Swish—a widely used mobile online payment application through which most Swedes prefer to make their donations to charity—each day of 2019 and 2020. Spontaneous charitable donations were operationalized as Swish‐payments to numbers starting with 90, as this number is a nationally acknowledged quality control label that is provided to all fundraising operations that are monitored by the Swedish Fundraising Control. The results show that the Swish‐donations fluctuated substantially depending on season (less donations in January–February and during the summer months, and more donations in April–May and during the last months of the year) and specific events (peaks in Swish‐donations often coincided with televised charity fundraising galas). Interrupted time‐series analyses revealed that spontaneous donations were overall unaffected by the pandemic outbreak.

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A comparative study between conventional ARMA and Fourier ARMA in modeling and forecasting wind speed data

Cited by 7 publications

References 11 publications

Spatial and seasonal variations in atmospheric aerosols over Nigeria: Assessment of influence of intertropical discontinuity movement

Spatial and seasonal variations in atmospheric aerosols over Nigeria: Assessment of influence of intertropical discontinuity movement

Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models

Spontaneous charitable donations in Sweden before and after COVID: A natural experiment

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