scholarly journals Time Series Analysis of Climatic Variables in Peninsular Spain. Trends and Forecasting Models for Data between 20th and 21st Centuries

Climate ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 119
Author(s):  
Pitshu Mulomba Mukadi ◽  
Concepción González-García
Keyword(s):  
Time Series ◽  
Temperature Series ◽  
Climatic Variables ◽  
Monthly Precipitation ◽  
Agricultural Crops ◽  
Arima Models ◽  
Cover Time ◽  
Pests And Diseases ◽  
Historical Series ◽  
Preliminary Phase

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops.

scholarly journals An improved ARIMA model for precipitation simulations

2014 ◽  
Vol 21 (6) ◽  
pp. 1159-1168 ◽  
Author(s):  
H. R. Wang ◽  
C. Wang ◽  
X. Lin ◽  
J. Kang
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Arima Model ◽  
Series Data ◽  
Monthly Precipitation ◽  
Monthly Variation ◽  
Arima Models ◽  
Hydrologic Variable

Abstract. Auto regressive integrated moving average (ARIMA) models have been widely used to calculate monthly time series data formed by interannual variations of monthly data or inter-monthly variation. However, the influence brought about by inter-monthly variations within each year is often ignored. An improved ARIMA model is developed in this study accounting for both the interannual and inter-monthly variation. In the present approach, clustering analysis is performed first to hydrologic variable time series. The characteristics of each class are then extracted and the correlation between the hydrologic variable quantity to be predicted and characteristic quantities constructed by linear regression analysis. ARIMA models are built for predicting these characteristics of each class and the hydrologic variable monthly values of year of interest are finally predicted using the modeled values of corresponding characteristics from ARIMA model and the linear regression model. A case study is conducted to predict the monthly precipitation at the Lanzhou precipitation station in Lanzhou, China, using the model, and the results show that the accuracy of the improved model is significantly higher than the seasonal model, with the mean residual achieving 9.41 mm and the forecast accuracy increasing by 21%.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

Identification of Synchronicity in Deterministic Chaotic Attractors for the Downscaling Process in the Bogotá River Basin

2021 ◽  
Author(s):  
Santiago Duarte ◽  
Gerald Corzo ◽  
Germán Santos
Keyword(s):  
Time Series ◽  
Time Delay ◽  
River Basin ◽  
Spatial Scales ◽  
Climatic Variables ◽  
Chaotic Attractors ◽  
Multiple Attractors ◽  
Nearest Neighbours ◽  
Basin Scale ◽  
Non Linear

&lt;p&gt;Bogot&amp;#225;&amp;#8217;s River Basin, it&amp;#8217;s an important basin in Cundinamarca, Colombia&amp;#8217;s central region. Due to the complexity of the dynamical climatic system in tropical regions, can be difficult to predict and use the information of GCMs at the basin scale. This region is especially influenced by ENSO and non-linear climatic oscillation phenomena. Furthermore, considering that climatic processes are essentially non-linear and possibly chaotic, it may reduce the effectiveness of downscaling techniques in this region.&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we try to apply chaotic downscaling to see if we could identify synchronicity that will allow us to better predict. It was possible to identify clearly the best time aggregation that can capture at the best the maximum relations between the variables at different spatial scales. Aside this research proposes a new combination of multiple attractors. Few analyses have been made to evaluate the existence of synchronicity between two or more attractors. And less analysis has considered the chaotic behaviour in attractors derived from climatic time series at different spatial scales.&amp;#160;&lt;/p&gt;&lt;p&gt;Thus, we evaluate general synchronization between multiple attractors of various climate time series. The Mutual False Nearest Neighbours parameter (MFNN) is used to test the &amp;#8220;Synchronicity Level&amp;#8221; (existence of any type of synchronization) between two different attractors. Two climatic variables were selected for the analysis: Precipitation and Temperature. Likewise, two information sources are used: At the basin scale, local climatic-gauge stations with daily data and at global scale, the output of the MPI-ESM-MR model with a spatial resolution of 1.875&amp;#176;x1.875&amp;#176; for both climatic variables (1850-2005). In the downscaling process, two RCP (Representative Concentration Pathways)&amp;#160; scenarios are used, RCP 4.5 and RCP 8.5.&lt;/p&gt;&lt;p&gt;For the attractor&amp;#8217;s reconstruction, the time-delay is obtained through the&amp;#160; Autocorrelation and the Mutual Information functions. The False Nearest Neighbors method (FNN) allowed finding the embedding dimension to unfold the attractor. This information was used to identify deterministic chaos at different times (e.g. 1, 2, 3 and 5 days) and spatial scales using the Lyapunov exponents. These results were used to test the synchronicity between the various chaotic attractor&amp;#8217;s sets using the MFNN method and time-delay relations. An optimization function was used to find the attractor&amp;#8217;s distance relation that increases the synchronicity between the attractors.&amp;#160; These results provided the potential of synchronicity in chaotic attractors to improve rainfall and temperature downscaling results at aggregated daily-time steps. Knowledge of loss information related to multiple reconstructed attractors can provide a better construction of downscaling models. This is new information for the downscaling process. Furthermore, synchronicity can improve the selection of neighbours for nearest-neighbours methods looking at the behaviour of synchronized attractors. This analysis can also allow the classification of unique patterns and relationships between climatic variables at different temporal and spatial scales.&lt;/p&gt;

PHENOLOGY, REPRODUCTIVE BIOLOGY AND SPATIAL DISTRIBUTION OF Chresta scapigera (LESS.) Gardner (ASTERACEAE)

2021 ◽  
Vol 25 (03) ◽  
pp. 710-721
Author(s):  
Écio Souza Diniz ◽  
◽  
Rodolfo Oliveira Costa ◽  
Larissa Areal Carvalho Müller ◽  
Jan Thiele ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Reproductive Biology ◽  
Floral Biology ◽  
The Self ◽  
Climatic Variables ◽  
Campo Rupestre ◽  
Climatic Data ◽  
Monthly Precipitation ◽  
Leaf Emergence ◽  
Self Pollination

Chrestas capigera (Less.) Gardner is an important medicinal herb which, however, has been poorly studied for its biology and ecology. This study aimed to investigate its phenology, floral biology, reproductive biology (self-pollination tests), spatial distribution and correlations between phenophases and climatic data in two sites (Cerrado stricto sensu and Campo rupestre) in southern Minas Gerais, Brazil. From August of 2012 to August of 2013, we monitored phenophase occurrence for 70 individuals: emission of new leaves, flowering, production of immature fruits, and mature fruits. Floral anthesis occurred during daytime and remained all day until fruit formation. Peak leaf emergence was observed in April, correlating with minimum monthly temperature and mean monthly precipitation. Flowering and green fruit peaked in May and June, respectively, and correlated negatively with all climatic variables. Mature fruits peaked in June, but did not correlate significantly with any of the climatic variables. However, no difference was found between the two sites regarding the timing of phenophases. The spatial distribution pattern of individuals within sites was random. The self-pollination tests showed that the individuals pollinated and fertilized themselves. Our findings allow us to conclude that the phenology of C. scapigera has pronounced phenological seasonality with reproductive peak activities in the drier and colder season, which is congruent with the self-pollination and anemochoric dispersion strategy.

scholarly journals AVALIAÇÃO DA DISPONIBILIDADE HÍDRICA NA SUB-BACIA DO BOI BRANCO ATRAVÉS DO BALANÇO HÍDRICO CLIMATOLÓGICO E DE CULTIVO

Irriga ◽  
2017 ◽  
Vol 22 (1) ◽  
pp. 1-17
Author(s):  
Mariana Alexandre de Lima Sales ◽  
RODRIGO MÁXIMO SÁNCHEZ ROMÁN ◽  
LEONOR RODRÍGUEZ SINOBAS ◽  
RAIMUNDO NONATO FARIAS MONTEIRO ◽  
JOÃO VICTOR RIBEIRO DA SILVA DE SOUZA
Keyword(s):  
Water Balance ◽  
Water Deficit ◽  
Environmental Planning ◽  
Water Cycle ◽  
Crop Coefficient ◽  
Monthly Precipitation ◽  
Agricultural Planning ◽  
Historical Series ◽  
Average Monthly Precipitation ◽  
E Mail

AVALIAÇÃO DA DISPONIBILIDADE HÍDRICA NA SUB-BACIA DO BOI BRANCO ATRAVÉS DO BALANÇO HÍDRICO CLIMATOLÓGICO E DE CULTIVO  MARIANA ALEXANDRE DE LIMA SALES1; RODRIGO MÁXIMO SÁNCHEZ ROMÁN2; LEONOR RODRÍGUEZ SINOBAS3; RAIMUNDO NONATO FARIAS MONTEIRO4; JOÃO VICTOR RIBEIRO DA SILVA DE SOUZA5. 1 Tecnóloga em Irrigação e Drenagem, Doutoranda em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. de Irrigação e Drenagem, Prof. Doutor FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu, SP. Fone: (14) 3711-7100. E-mail: [email protected] Eng. Agrônoma, Profa. Doutora ETSIA/UPM, Ciudad Universitaria, 28040 Madri, Espanha. e-mail: [email protected] Tecnólogo em Recursos Hídricos/Irrigação, Doutor em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. Agrônomo, Doutorando em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected].  1 RESUMO Uma das formas de contabilizar a quantidade de água de um determinado sistema é por meio do balanço hídrico, o qual é uma importante ferramenta para o processo de avaliação do ciclo da água em uma determinada região. O objetivo deste trabalho foi determinar o balanço hídrico na sub-bacia hidrográfica do Boi Branco-SP, para servir como ferramenta ao planejamento hidroagrícola e ambiental da região. Para o balanço hídrico climatológico, utilizaram-se dados da série histórica da região (1971 a 1995). Os dados de evapotranspiração foram estimados pelo método de Thornthwaite. O balanço hídrico climatológico mostrou déficit hídrico total anual de 10,1 mm, e um excedente de 319,7 mm, tendo no mês de janeiro um excedente de 92,6 mm, para a precipitação média mensal; com a precipitação efetiva mensal com probabilidade de 75%, déficit hídrico no solo é de 238,8 mm e o excedente 56,8 mm. Quando se adiciona a esses dados os das culturas implantadas na área de estudo, como coeficiente de cultivo e fator de depleção da umidade do solo, observa-se que todas as culturas do estudo apresentaram déficit hídrico em todos os meses em que estiveram no campo. Palavras-chave: Planejamento hidroagrícola, capacidade de água disponível no solo, evapotranspiração.  SALES, M. A. L.; SÁNCHEZ-ROMÁN, R. M.; SONOBAS, L. R.; MONTEIRO, R. N. F.; SOUZA, J. V. R. S.ASSESSMENT OF WATER AVAILABILITY AT BOI BRANCO WATERSHED   THROUGH CLIMATIC WATER BALANCE AND GROWING  2 ABSTRACT One way to calculate the amount of water in a determined system is by means of the water balance, an important tool for the assessment of the water cycle in a specific region. The main goal of this work was to establish the water balance in the watershed Boi Branco-SP, so that it can be used as a tool for the hydro-agricultural and environmental planning of the region. For the climatic water balance, data of the historical series of the region (1971 - 1995) were used. Evapotranspiration data were estimated by the Thornthwaite method. The climatic water balance showed  total annual water deficit  of 10.1 mm, and surplus of 319.7 mm, with January presenting surplus  of  92.6 in the average monthly precipitation; given that the effective monthly precipitation presenting probability of 75%,  water deficit  in the soil  is 238.8 mm and surplus is 56.8 mm. When these data are added to the ones of the crop, as a crop coefficient and soil humidity depletion factor, it is observed that all crops studied showed water deficit  in all the months covered. Keyword: Water agricultural planning, water capability available in the soil, evapotranspiration.

scholarly journals Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

2020 ◽  
pp. 100458
Author(s):  
Michelle Li Ern Ang ◽  
Dirk Arts ◽  
Danielle Crawford ◽  
Bonifacio V. Labatos ◽  
Khanh Duc Ngo ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Analysis ◽  
Google Earth ◽  
Cover Time ◽  
Web Based ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals A case study of the residual-based cointegration procedure

2003 ◽  
Vol 7 (1) ◽  
pp. 29-48
Author(s):  
Riccardo Biondini ◽  
Yan-Xia Lin ◽  
Michael Mccrae
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Real Life ◽  
Arima Models ◽  
Financial Time ◽  
Long Run ◽  
Data Generating Process ◽  
Finance Theory ◽  
The Relationship

The study of long-run equilibrium processes is a significant component of economic and finance theory. The Johansen technique for identifying the existence of such long-run stationary equilibrium conditions among financial time series allows the identification of all potential linearly independent cointegrating vectors within a given system of eligible financial time series. The practical application of the technique may be restricted, however, by the pre-condition that the underlying data generating process fits a finite-order vector autoregression (VAR) model with white noise. This paper studies an alternative method for determining cointegrating relationships without such a pre-condition. The method is simple to implement through commonly available statistical packages. This ‘residual-based cointegration’ (RBC) technique uses the relationship between cointegration and univariate Box-Jenkins ARIMA models to identify cointegrating vectors through the rank of the covariance matrix of the residual processes which result from the fitting of univariate ARIMA models. The RBC approach for identifying multivariate cointegrating vectors is explained and then demonstrated through simulated examples. The RBC and Johansen techniques are then both implemented using several real-life financial time series.

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