Representing the precipitation regime by means of Fourier series

Laguardia, Giovanni

doi:10.1002/joc.2169

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…The park encompasses medium altitude moist evergreen forests as well as swamps, grasslands, woodland thickets and colonizing shrubs (Struhsaker 1997, Chapman andLambert 2000) and is surrounded by agricultural land, including tea plantations and small farms. The mean annual rainfall is 1696 mm with two distinct rainy seasons (1990-2011C. A. Chapman, unpublished data;Lambert 2000, Stampone et al 2011).…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Tropical phenology: bi‐annual rhythms and interannual variation in an Afrotropical butterfly assemblage

et al. 2013

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Abstract. Temporal variation and phenology of tropical insect communities and the role of environmental factors controlling this variation is poorly understood. A better understanding is needed, for example, to predict the effects of climate change on tropical insect communities and to assess the longterm persistence of tropical communities. We studied seasonal and inter-annual variation in tropical fruitfeeding butterflies by exploiting a unique 137-month abundance time series of .100 species, sampled at 22 locations in the medium altitude montane rain forest of Kibale National Park, western Uganda. Precipitation peaked twice per year, about 20 d after each equinox. Vegetation greenness peaked approximately 33 d later. Species richness and abundance of butterflies peaked about 2 and 3 months, respectively, after the greenness peak. Furthermore, temporal shifts in peaks of butterfly abundances of each 6-month cycle positively correlated with temporal shifts in peaks of vegetation greenness approximately three months before. The butterfly assemblages of ENSO warm phase years differed significantly from assemblages of the other years. To our knowledge this is the first elucidation of bi-annual rhythms in butterfly assemblages. Host plant availability could explain the seasonal cycles in butterfly abundance and species richness, because the 3-month lag observed matches with the egg-to-adult development time in the studied species.

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Section: Study Area and Data Collectionmentioning

confidence: 99%

Tropical phenology: bi‐annual rhythms and interannual variation in an Afrotropical butterfly assemblage

et al. 2013

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“…The second element of functional clustering is the clustering algorithm. In the literature, k ‐means algorithm has often been used as, for example, in an application to precipitation data (Laguardia, ). Here we prefer a k ‐medoids approach and, in particular, the PAM (Kaufman and Rousseeuw, ).…”

Section: Methodsmentioning

confidence: 99%

Functional exploratory data analysis for high‐resolution measurements of urban particulate matter

et al. 2016

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In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugia (Central Italy). An OPC (Optical Particle Counter) is integrated on a cabin of the Minimetrò, an urban transportation system, that moves along a monorail on a line transect of the town. The OPC takes a sample of air every six seconds and counts the number of particles of urban aerosols with a diameter between 0.28 μm and 10 μm and classifies such particles into 21 size bins according to their diameter. Here, we adopt a 2D functional data representation for each of the 21 spatiotemporal series. In fact, space is unidimensional since it is measured as the distance on the monorail from the base station of the Minimetrò. FDA allows for a reduction of the dimensionality of each dataset and accounts for the high space-time resolution of the data. Functional cluster analysis is then performed to search for similarities among the 21 size channels in terms of their spatiotemporal pattern. Results provide a good classification of the 21 size bins into a relatively small number of groups (between three and four) according to the season of the year. Groups including coarser particles have more similar patterns, while those including finer particles show a more different behavior according to the period of the year. Such features are consistent with the physics of atmospheric aerosol and the highlighted patterns provide a very useful ground for prospective model-based studies.

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“…In the cases involving time series of random phenomena, the use of the Fourier series analysis method is not fully adequate. However, the use of Fourier series analysis in stochastic phenomena may not be impossible, as evidenced by a study from Laguardia (2011), who used a Fourier series analysis model for representing the precipitation regime in a certain location and predicting it in ungauged locations. The interpolation of the two harmonics Fourier series parameters derived from observed data have been demonstrated to be as accurate as the one obtained through the application of the month-by-month estimation based on observed data, with average RMSEs of 17.53 and 15.97 mm and correlation coefficients of 0.909 and 0.921.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecast of sales of selected food products in retail using Fourier series analysis and non-linear regression

Kačmáry,

Bindzár,

Kovalčík

et al. 2024

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Purpose The purpose of this paper is to apply and verify Fourier series analysis in combination with non-linear regression as a tool of forecasting and planning of inputs in the logistics process of a retail chain store. Design/methodology/approach For many popular products, a significant effect of seasonality of sales is expected; therefore, the method of Fourier series was chosen as one of the main forecast calculation techniques. However, the use of this method directly for forecasting sales has a limitation in the form of a complete reconstruction of the shape of the curve from of the given monitored time. Thus, the forecast is based only on the significant harmonic components from the Fourier series analysis that will participate in forecast forming. In addition, to respect the trend of series, the results of Fourier series analysis are combined with the non-linear regression. Findings The results showed that the number of significant harmonic components from the Fourier series analysis is suitable to reflect the future behaviour of the sale in standard market conditions. Forecasting of the sale and accurate purchase planning of goods has a positive effect on reducing the waste of unsold products after their shelf and on increasing of a customer satisfaction. Research limitations/implications This study has an application in a certain period of time (relatively calm behaviour of the food market) and only for a certain region. Therefore, it is not possible to generalize these results as the behaviour of consumers, e.g. within the state. It will also be interesting to monitor and forecast sales of other food items. Practical implications This provides a practical and relatively simple tool for implementing or improving the process of forecasting seasonally dependent products in the food industry. Originality/value This study shows the possibility of forecast that is based on adding the significant harmonic components from the Fourier series analysis to form forecast with the non-linear regression.

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Representing the precipitation regime by means of Fourier series

Cited by 8 publications

References 12 publications

Tropical phenology: bi‐annual rhythms and interannual variation in an Afrotropical butterfly assemblage

Tropical phenology: bi‐annual rhythms and interannual variation in an Afrotropical butterfly assemblage

Functional exploratory data analysis for high‐resolution measurements of urban particulate matter

Forecast of sales of selected food products in retail using Fourier series analysis and non-linear regression

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