Climatic Forecasting of Wind and Waves Using Fuzzy Inference Systems

2017 ◽  
Author(s):  
Christos N. Stefanakos ◽  
Erik Vanem
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
North Atlantic Ocean ◽  
Offshore Structures ◽  
Mean Value ◽  
Initial Time ◽  
The North ◽  
Residual Part ◽  
Time Series Modelling ◽  
Inference Systems

Wind and wave climatic simulations are of great interest in a number of different applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. In a climate change perspective, projections of such simulations to a future climate are of great importance for risk management and adaptation purposes. This work investigates the applicability of FIS/ANFIS models for climatic simulations of wind and wave data. The models are coupled with a nonstationary time series modelling, which decomposes the initial time series into a seasonal mean value and a residual part multiplied by a seasonal standard deviation. In this way, the nonstationary character is first removed before starting the fuzzy forecasting procedure. Then, the FIS/ANFIS models are applied to the stationary residual part providing us with more unbiased climatic estimates. Two long-term datasets for an area in the North Atlantic Ocean are used in the present study, namely NORA10 (57 years) and ExWaCli (30 years in the present and 30 years in the future). Two distinct experiments have been performed to simulate future values of the time series in a climatic scale. The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.

scholarly journals Information technology for forecasting the financial results of insurance companies

2021 ◽  
Vol 3 (2) ◽  
pp. 87-93
Author(s):  
K. M. Berezka ◽  
◽  
O. V. Kneysler ◽  
N. Ya. Spasiv ◽  
H. M. Kulyna ◽  
...  
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Mean Value ◽  
Insurance Companies ◽  
Rational Approach ◽  
Percentage Error ◽  
Software Module ◽  
Financial Decisions ◽  
Time Series Modelling ◽  
Financial Result

The purpose of time series modelling is to predict future indicators based on the study and analysis of past and present data. Various time series methods are used for forecasting. The article uses econometric extrapolation research methods. Analyzed scientific works are related to extrapolation methods for forecasting time series. The dynamics of the financial formation related to results of Ukrainian insurance companies by the types of their activities have been analyzed. The main factors that determine the effectiveness are determined. It was found that the most rational approach to short-term forecasting of the financial results of insurers is the use of exponential smoothing. The optimal parameters are selected for the model of exponential smoothing of the first and second order by the method on the grid. The following indicators of the quality of the model were used: the mean value of the standard deviation of the model error to the actual data, Theils coefficient of discrepancy, mean absolute percentage error MARE. The net financial result of the activities of Ukrainian insurers was predicted, the lower and upper bounds of the forecast for 2021 for a reliability level of 0.95. To predict the net financial result of the activities of Ukrainian insurers, statistical data for 10 years from 2011 to 2020 were used, the financial results of the main (insurance and other operating) activities before tax, the results of financial activities before tax, the financial results of other ordinary activities (extraordinary events) before tax, income tax. The prototype of the software module for predicting the financial performance of insurance companies was developed in Statistica and Excel. Forecasting results based on the use of econometric modelling make it possible to identify permanent positive shifts in the domestic insurance market and the activities of insurers on it; to confirm the effectiveness of the adopted strategic and tactical financial decisions of insurance companies; to increase the efficiency of insurers management based on the results of quantitative determination the degree of influence of each factor on the formation of the financial results related to their activities; to identify trends in the development of the situation in the future, to more accurately form a set of measures to maximize profits and minimize costs of insurance companies to ensure guarantees of reliable insurance protection and satisfy the interests of their owners. Keywords: financial results; insurance companies; net financial result; exponential smoothing; time series; econometric forecasting methods.

Demand forecasting in the beauty industry using fuzzy inference systems

2020 ◽  
Vol 15 (4) ◽  
pp. 1389-1417
Author(s):  
Ricardo Felicio Souza ◽  
Peter Wanke ◽  
Henrique Correa
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Theory And Practice ◽  
Forecast Errors ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Content Type ◽  
Forecasting Methods ◽  
Inference Systems

Purpose This study aims to analyze the performance of four different fuzzy inference system-based forecasting tools using a real case company. Design/methodology/approach The forecasting tools were tested using 27 products of the nail polish line of a multinational beauty company and the performance of said tools was compared to those of the company’s previous forecasting methods that were basically qualitative (informal and intuition-based). Findings The performance of the methods analyzed was compared by using mean absolute percentage error. It was possible to determine the characteristics and conditions that make each model the best for each situation. The main takeaways were that low kurtosis, negatively skewed demand time-series and longer horizon forecasts that favor the fuzzy inference system-based models. Besides, the results suggest that the fuzzy forecasting tools should be preferred for longer horizon forecasts over informal qualitative methods. Originality/value Notwithstanding the proposed hybrid modeling approach based on fuzzy inference systems, our research offers a relevant contribution to theory and practice by shedding light on the segmentation and selection of forecasting models, both in terms of time-series characteristics and forecasting horizon. The proposed fuzzy inference systems showed to be particularly useful not only when time-series distributions present no clear central tendency (that is, they are platykurtic or dispersed around a large plateau around the median, which is the characteristic of negative kurtosis), but also when mode values are greater than median values, which in turn are greater than mean values. This large tail to the left (negative skewness) is typical of successful products whose sales are ramping up in early stages of their life cycle. For these, fuzzy inference systems may help managers screen out forecast bias and, therefore, lower forecast errors. This behavior also occurs when managers deal with forecasts of longer horizons. The results suggest that further research on fuzzy inference systems hybrid approaches for forecasting should emphasize short-term forecasting by trying to better capture the “tribal” managerial knowledge instead of focusing on less dispersed and slower moving products, where the purely qualitative forecasting methods used by managers tend to perform better in terms of their accuracy.

scholarly journals Detecting quasi-oscillations in the monthly precipitation regimes of the Iberian Peninsula

2003 ◽  
Vol 21 (3) ◽  
pp. 819-832 ◽  
Author(s):  
L. Morala ◽  
A. Serrano ◽  
J. A. Garcia
Keyword(s):  
Time Series ◽  
High Pressure ◽  
Iberian Peninsula ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Singular Spectrum Analysis ◽  
Monthly Precipitation ◽  
The North ◽  
Precipitation Regimes ◽  
The North Atlantic

Abstract. A spectral analysis of the time series corresponding to the main monthly precipitation regimes of the Iberian Peninsula was performed using two methods, the Multi-Taper Method and Monte Carlo Singular Spectrum Analysis. The Multi-Taper Method gave a preliminary view of the presence of signals in some of the time series. Monte Carlo Singular Spectrum Analysis discriminated between potential oscillations and noise. From the results of the two methods it is concluded that there exist three significant quasi-oscillations at the 95% level of confidence: a 5.0 year quasi-oscillation and a long-term trend in the Atlantic pattern of March, a 3.2 year quasi-oscillation in the Cantabrian pattern of January, and a 4.0 year quasi-oscillation in the Catalonian pattern of February. These quasi-oscillations might be related to climatic variations with similar periodicities over the North Atlantic Ocean. The possible simultaneity of high values of precipitation generated by the significant quasi-oscillations and high sea–level pressures was studied by means of composite maps. It was found that high values of precipitation generated by the oscillations of the Atlantic patterns of January and March exist simultaneously with a specific high pressure structure over the North Atlantic Ocean, that allow cyclonic perturbations to cross the Iberian Peninsula. During the non-wet years, this high pressure structure moves northwards, keeping the track of the low pressure centers to the north, far from the Iberian Peninsula. On the other hand, high values of precipitation generated by the oscillation of the Cantabrian pattern of January exist simultaneously with a high pressure structure over the Galicia region and the Cantabrian Sea, that allow a northerly flow over the region. Also, a positive trend in the NAO index for March has been found, starting in the sixties, which is not evident for other winter months. This trend agrees with the decreasing trend found in the March Atlantic pattern.Key words. Meteorology and atmospheric dynamics (climatology; precipitation) Oceanography: general (climate and interannual variability)

scholarly journals THE USE OF FUZZY RULE-BASED SYSTEMS IN DETERMINING HORIZONTAL PCO PARAMETERS OF GNSS ANTENNAS

2018 ◽  
Vol 24 (3) ◽  
pp. 367-382
Author(s):  
Nassau de Nogueira Nardez ◽  
Cláudia Pereira Krueger ◽  
Rosana Sueli da Motta Jafelice ◽  
Marcio Augusto Reolon Schmidt
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Training Data ◽  
Data Set ◽  
Rule Based ◽  
The North ◽  
The Difference ◽  
Inference Systems ◽  
Antenna Calibration ◽  
Rule Based Systems

Abstract Knowledge concerning Phase Center Offset (PCO) is an important aspect in the calibration of GNSS antennas and has a direct influence on the quality of high precision positioning. Studies show that there is a correlation between meteorological variables when determining the north (N), east (E) and vertical Up (H) components of PCO. This article presents results for the application of Fuzzy Rule-Based Systems (FRBS) for determining the position of these components. The function Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was used to generate FRBS, with the PCO components as output variables. As input data, the environmental variables such as temperature, relative humidity and precipitation were used; along with variables obtained from the antenna calibration process such as Positional Dilution of Precision and the multipath effect. An FRBS was constructed for each planimetric N and E components from the carriers L1 and L2, using a training data set by means of ANFIS. Once the FRBS were defined, the verification data set was applied, the components obtained by the FRBS and Antenna Calibration Base at the Federal University of Paraná were compared. For planimetric components, the difference was less than 1.00 mm, which shows the applicability of the method for horizontal components.

scholarly journals Prevalence of the Chloroflexi-Related SAR202 Bacterioplankton Cluster throughout the Mesopelagic Zone and Deep Ocean

2004 ◽  
Vol 70 (5) ◽  
pp. 2836-2842 ◽  
Author(s):  
R. M. Morris ◽  
M. S. Rappé ◽  
E. Urbach ◽  
S. A. Connon ◽  
S. J. Giovannoni
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
16S Rrna ◽  
Atlantic Ocean ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
16S Rrna Genes ◽  
Rrna Genes ◽  
The North ◽  
The North Atlantic

ABSTRACT Since their initial discovery in samples from the north Atlantic Ocean, 16S rRNA genes related to the environmental gene clone cluster known as SAR202 have been recovered from pelagic freshwater, marine sediment, soil, and deep subsurface terrestrial environments. Together, these clones form a major, monophyletic subgroup of the phylum Chloroflexi. While members of this diverse group are consistently identified in the marine environment, there are currently no cultured representatives, and very little is known about their distribution or abundance in the world's oceans. In this study, published and newly identified SAR202-related 16S rRNA gene sequences were used to further resolve the phylogeny of this cluster and to design taxon-specific oligonucleotide probes for fluorescence in situ hybridization. Direct cell counts from the Bermuda Atlantic time series study site in the north Atlantic Ocean, the Hawaii ocean time series site in the central Pacific Ocean, and along the Newport hydroline in eastern Pacific coastal waters showed that SAR202 cluster cells were most abundant below the deep chlorophyll maximum and that they persisted to 3,600 m in the Atlantic Ocean and to 4,000 m in the Pacific Ocean, the deepest samples used in this study. On average, members of the SAR202 group accounted for 10.2% (±5.7%) of all DNA-containing bacterioplankton between 500 and 4,000 m.

scholarly journals The Imprint of Strong-Storm Tracks on Winter Weather in North America

2018 ◽  
Vol 31 (5) ◽  
pp. 2057-2074 ◽  
Author(s):  
Katherine E. Lukens ◽  
Ernesto Hugo Berbery ◽  
Kevin I. Hodges
Keyword(s):  
North America ◽  
North Atlantic Ocean ◽  
Deep Convection ◽  
Storm Track ◽  
Economic Loss ◽  
Mean Value ◽  
Storm Tracks ◽  
Winter Weather ◽  
Near Surface ◽  
The North

Northern Hemisphere winter storm tracks and their relation to winter weather are investigated using NCEP CFSR data. Storm tracks are described by isentropic PV maxima within a Lagrangian framework; these correspond well with those described in previous studies. The current diagnostics focus on strong-storm tracks, which comprise storms that achieve a maximum PV exceeding the mean value by one standard deviation. Large increases in diabatic heating related to deep convection occur where the storm tracks are most intense. The cyclogenesis pattern shows that strong storms generally develop on the upstream sectors of the tracks. Intensification happens toward the eastern North Pacific and all across the North Atlantic Ocean, where enhanced storm-track-related weather is found. In this study, the relation of storm tracks to near-surface winds and precipitation is evaluated. The largest increases in storm-track-related winds are found where strong storms tend to develop and intensify, while storm precipitation is enhanced in areas where the storm tracks have their highest intensity. Strong storms represent about 16% of all storms but contribute 30%–50% of the storm precipitation in the storm-track regions. Both strong-storm-related winds and precipitation are prone to cause storm-related losses in the eastern U.S. and North American coasts. Over the oceans, maritime operations are expected to be most vulnerable to damage offshore of the U.S. coasts. Despite making up a small fraction of all storms, the strong-storm tracks have a significant imprint on winter weather in North America potentially leading to structural and economic loss.

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