scholarly journals Long-Range Prediction of the Shipping Season in Hudson Bay: A Statistical Approach

2007 ◽  
Vol 22 (5) ◽  
pp. 1063-1075 ◽  
Author(s):  
Adrienne Tivy ◽  
Bea Alt ◽  
Stephen Howell ◽  
Katherine Wilson ◽  
John Yackel
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Sea Ice ◽  
Multiple Linear Regression ◽  
Success Rate ◽  
Long Range ◽  
Low Frequency ◽  
Seasonal Forecasting ◽  
Hudson Bay ◽  
Physical Processes

Abstract Despite recent reductions in Arctic sea ice extent and the associated increase in both the recreational and commercial use of ice-infested waters, long-range prediction of operationally relevant sea ice parameters is an area of seasonal forecasting that has received little attention. Statistical methods that isolate and exploit empirical relationships between antecedent low-frequency climate variability and specific variables of interest are often used to solve seasonal forecasting problems. In this study, simple multiple linear regression (MLR) techniques are used to improve the skill of the seasonal (3-month lead) forecast of the breakup and clearing of sea ice along the shipping route through Hudson Bay that is issued each March by the Canadian Ice Service of Environment Canada. Using sea ice and climate data from 1972 to 2002, predictive MLR models are developed for the spring opening date of the shipping route and the latest expected opening date. A success rate of 77% over the 1972–2002 period for the opening date, from an MLR model that explains 76% of the variability in the original time series with a mean absolute error (MAE) of 0.38, is a marked improvement over the 48% success rate of the current analog methodology. The success rate of the model for the latest expected date is 87%; the modeled time series adequately represented interannual variability in the observed time series (r = 0.71) with a low MAE (MAE = 0.51). Results from a series of model diagnostics that include Monte Carlo simulations, cross validation, and analysis of residuals, suggest the final models are statistically valid and are not influenced by artificial skill. The main source of predictive skill in the model is winter low-frequency variability in North Atlantic sea surface temperatures and 500-mb geopotential heights; physical processes that may explain this link are presented. It is concluded that simple multiple linear regression techniques can be applied to generate use-specific seasonal forecasts of sea ice conditions and that the empirical knowledge gained in the model development may help elucidate or identify physical processes in the climate system.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR BERAS DI INDONESIA

2019 ◽  
Vol 11 (2) ◽  
pp. 183-201
Author(s):  
Yona Namira ◽  
Iskandar Andi Nuhung ◽  
Mudatsir Najamuddin
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
The Us ◽  
Rice Prices ◽  
Us Dollar ◽  
Spss Software

This study aims to 1) identify factors that affect the import of rice in Indonesia 2) analyze the influence of these factors on imports of rice in Indonesia. The data used in this research are time series data from 1994 to 2013 from the Central Statistics Agency (BPS), the Ministry of Agriculture, Ministry of Commerce, National Logistics Agency (Bulog), and Bank Indonesia. Multiple linear regression through SPSS software version 21 was employed to analyze the data. The test results together indicated the variables of productions, consumptions, stocks of rice, domestic rice prices, international rice prices and the rupiah against the US dollar affect the imports of rice in Indonesia.

scholarly journals Pengaruh Industri Sedang dan Besar Terhadap Tingkat Kemiskinan di Kabupaten Gresik Tahun 2002-2016

2021 ◽  
Vol 4 (1) ◽  
pp. 25-31
Author(s):  
Rohmatul Janah ◽  
Ida Nuraini
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Industrial Sector ◽  
Poverty Rate ◽  
Domestic Product ◽  
Series Data ◽  
Insignificant Effect ◽  
Poverty Levels

This research is aimed at studying the influence of medium and large industries on poverty levels in Gresik on 2002-2016. The variables used in this study is medium and large industries, a labour of medium and large industries, gross regional domestic product (GRDP) of industrial sector and poverty rate. The method used in this study used multiple linear regression and used time-series data. The results of this study simultaneously are the variables of the amount of medium and large industries, the labour medium and large industries, and the gross regional domestic product (GRDP) of the industrial sector to poverty rate is significant. While medium and large industries to poverty rate have negative and insignificant effect with a coefficient value of -0,208905. The labour of medium and large industries to poverty rate has a positive and significant effect with a coefficient value of 0,130822,  the gross regional domestic product (GRDP) of industrial to poverty rate has a negative and significant effect with a coefficient value of -0,169431.

scholarly journals PENGARUH INVESTASI DAN TENAGA KERJA TERHADAP PRODUK DOMESTIK REGIONAL BRUTO (PDRB) DAN PENDAPATAN ASLI DAERAH (PAD) KOTA BALIKPAPAN

2015 ◽  
Vol 5 (2) ◽  
pp. 159
Author(s):  
Johana Rosmalia ◽  
Rusdiah Iskandar ◽  
Fitriadi Fitriadi
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Pathway Analysis ◽  
Secondary Data ◽  
Strong Relationship ◽  
Domestic Product ◽  
Regression Formula

This study used secondary data in the form of time series which are analyzed using Pathway analysis with multiple linear regression formula. The purpose of this study was to determine the effect of investment and labor to gross regional domestic product (GRDP) and regional revenues in Balikpapan city.  The results of the study was shown that Y = 0.202 - 0.098 X1 + 0.244 X2 + 0.825 X3. The results showed that investment, labor and gross regional domestic product (GRDP) have jointly effect on the regional revenues in Balikpapan city. Partially; the investment has no significant effect on gross regional domestic product (GRDP), labor has significant effect on gross regional domestic product (GRDP), gross regional domestic product (GRDP) has significant effect to regional revenues in Balikpapan city, the investment has no significant effect on regional revenues in Balikpapan city, and labor has no significant effect on regional revenues in Balikpapan city. The contribution of investment, labor and gross regional domestic product (GRDP) variable was about 93.5 % and it means that those have very strong relationship; meanwhile, the rest, about 6.5 %, has been influenced by other factors.

scholarly journals Pengaruh Investasi Dan Inflasi Terhadap Jumlah Penduduk Miskin di Kabupaten Jember Tahun 2000-2015

2019 ◽  
Vol 6 (1) ◽  
pp. 81
Author(s):  
Abdul Latif Hamzah ◽  
Anifatul Hanim ◽  
Herman Cahyo
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Secondary Data ◽  
Poor People ◽  
Poverty Level ◽  
Total Investment ◽  
Year 2000 ◽  
Very High

Conditions in Jember Regency from year to year economic growth is quite high, but the poverty level is very high as well. This study aims to determine the effect of investment and inflation on the number of poor people in the district of Jember in 2000- 2015. The method used in this research is multiple linear regression. The data used are secondary data formed in time series, the data used include investment, inflation in Jember district for 16 years in the year 2000-2015. Based on the results of the research, it can be seen that investment variables do not significantly affect the number of poor people in Jember, while Inflation has a significant effect on the number of poor people in Jember. Keywords: total investment, inflation, and poor people.

scholarly journals A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yanpeng Zhang ◽  
Hua Qu ◽  
Weipeng Wang ◽  
Jihong Zhao
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Time Series Clustering ◽  
Proposed Model ◽  
High Order Time

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

scholarly journals Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

2009 ◽  
Vol 9 (14) ◽  
pp. 4537-4544 ◽  
Author(s):  
M. Lanfredi ◽  
T. Simoniello ◽  
V. Cuomo ◽  
M. Macchiato
Keyword(s):  
Time Series ◽  
Long Range ◽  
Power Law ◽  
Low Frequency ◽  
Atmospheric Temperature ◽  
Fluctuation Analysis ◽  
Climate Modelling ◽  
Scale Free ◽  
Statistical Confidence ◽  
Frequency Components

Abstract. This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA) carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

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