scholarly journals The Temperature of Japan Modelling Japan’s Average Monthly Temperature from 1901 to 2015

2018 ◽  
Vol 2 (3) ◽  
pp. 224-228
Author(s):  
Batol Shiwa Hashimi ◽  
Aissa Boudjella ◽  
Wagma Saboor
Keyword(s):  
Time Series ◽  
Polynomial Regression ◽  
Polynomial Function ◽  
Average Difference ◽  
Time Intervals ◽  
Average Temperature ◽  
Monthly Average ◽  
The Past ◽  
Temperature Trends ◽  
The Relationship

The purpose of this investigation is to examine the variation of temperature in Japan over the past 114 years. The historical dataset of the monthly average temperature from 1901 to 2015 were analyzed. The relationship between temperature and time during the four time intervals, i.e (1901 -1930), (1931-1960), (1961-1990) and (1991-2015) is described using a new analytical model based on the last –square method of estimation. We accurately fit a polynomial regression trend of degree 4 to the time series to describe the temperature variation. The results show the average difference of temperature between 2015 and 1901 increases about 0.97 °C. The average monthly difference between the maximum and minimum temperature was approximately 2.11 °C. This approach of modeling temperature using regression form significantly simplifies the data analysis. The information from data, namely the variation of the temperature, maybe be obtained from the extracted parameters such as slope, y-intercept, and the coefficients of polynomial function that are a function of time. More importantly, the parameters that describe the time variation temperature trends over 115 years obtained with a high R-squared do not vary significantly. This is in agreement with the Earth’s average temperature that has climbed to more 1 oC.

scholarly journals Modelling Afghanistan’s Average Monthly Temperature from 1901 to 2015

2019 ◽  
Vol 1 (3) ◽  
pp. 61-66
Author(s):  
Emal Ismail ◽  
Aissa Boudjella
Keyword(s):  
Minimum Temperature ◽  
Polynomial Regression ◽  
Drought Risk ◽  
Climate Data ◽  
Nature Protection ◽  
Human Welfare ◽  
Policy Makers ◽  
Average Temperature ◽  
Temperature Trends ◽  
The Relationship

The purpose of this investigation is to examine the variation of temperature in Afghanistan over the past 114 years. The historical dataset of the monthly average temperature from 1901 to 2015 were analyzed. The relationship between temperature and time during the four time intervals, i.e. (1901 -1930), (1931-1960), (1961-1990) and (1991-2015) is presented using a new analytical model based on the last –square method of estimation. We accurately fit a polynomial regression trend of degree 4 to the time series to describe the temperature variation. The results show the average difference of temperature between 2015 and 1901 increases about 1.03 °C. The average monthly difference between the maximum and minimum temperature was approximately 3.66 °C and the average monthly difference between the maximum and minimum temperature during these periods is approximately about 1.31 °C. This approach of modeling temperature using regression form significantly simplifies the data analysis. The information from data, namely the variation of the temperature, maybe be obtained from the extracted parameters such as slope, y-intercept, and the coefficients of polynomial function that are a function of time. More importantly, the parameters that describe the time variation temperature trends over 115 years obtained with a high R-squared do not vary significantly. This is in agreement with the Earth’s average temperature that has climbed to more 1 °C  The evaluation of Afghanistan’s past climate data can be extremely important for understanding how climate has varied along with their possible predictable outcomes that come with increasing drought risk due to gradual increase of temperature. These results may be useful for environmental policy makers in comprehension of climate change in Afghanistan. The results can help develop appropriate strategies for the environment, and regulate resource use or pollution reduction to promote human welfare and/or nature protection.

The Relationship between Energy Consumption and Climate Change in Shanghai

2012 ◽  
Vol 524-527 ◽  
pp. 2388-2393 ◽  
Author(s):  
Nan Wang ◽  
Mahjoub Elnimeiri
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Energy Use ◽  
Fossil Fuel ◽  
Average Temperature ◽  
The Past ◽  
Strong Positive Relationship ◽  
The Relationship ◽  
Annual Average Temperature ◽  
Fossil Fuel Energy

The phenomenon of climate change is becoming a global problem. One of the most important reasons of climate change is the increase in CO2 levels due to emissions from fossil fuel energy use in daily human activities. This research will use the data of the annual average temperature and energy consumption in the past 41 years of Shanghai, the largest city in China, to establish the statistical relationship between climate change and energy consumption. It is found that there is a strong positive relationship between climate change and energy consumption in Shanghai. The phenomenon of climate change could be controlled by reducing excessive energy consumption in people’s daily life. Furthermore, this paper will also discuss the reason of such relationship, and provide suggesstions of saving energy and protecting our environment.

scholarly journals Finite-sample properties of estimators for first and second order autoregressive processes

2021 ◽  
Author(s):  
Sigrunn H. Sørbye ◽  
Pedro G. Nicolau ◽  
Håvard Rue
Keyword(s):  
Time Series ◽  
Polynomial Regression ◽  
Gaussian Copula ◽  
Finite Sample ◽  
Coefficient Estimates ◽  
Ecological Data ◽  
Data Set ◽  
Finite Sample Properties ◽  
The Relationship ◽  
Corrected Coefficient

AbstractThe class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily available and routinely fitted using freely available statistical software like . A potential problem is that commonly applied estimators for the coefficients of AR processes are severely biased when the time series are short. This paper studies the finite-sample properties of well-known estimators for the coefficients of stationary AR(1) and AR(2) processes and provides bias-corrected versions of these estimators which are quick and easy to apply. The new estimators are constructed by modeling the relationship between the true and originally estimated AR coefficients using weighted orthogonal polynomial regression, taking the sampling distribution of the original estimators into account. The finite-sample distributions of the new bias-corrected estimators are approximated using transformations of skew-normal densities, combined with a Gaussian copula approximation in the AR(2) case. The properties of the new estimators are demonstrated by simulations and in the analysis of a real ecological data set. The estimators are easily available in our accompanying -package for AR(1) and AR(2) processes of length 10–50, both giving bias-corrected coefficient estimates and corresponding confidence intervals.

Regional rural and structural transformations and farmer's income in the past four decades in China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jikun Huang ◽  
Pengfei Shi
Keyword(s):  
Time Series ◽  
Design Methodology ◽  
Structural Transformations ◽  
Policy Implications ◽  
Rural Transformation ◽  
Content Type ◽  
The Past ◽  
Rural Income ◽  
Graphic Illustration ◽  
The Relationship

PurposeThe purposes of this paper are to analyze the path and speed of rural transformation (RT) and explore the relationship between farmer's income and RT as well as structural transformation (ST) and typology of RT in the past four decades in China.Design/methodology/approachBased on the major indicators of RT and ST, graphic illustration is used to analyze the relationships between these indicators and farmer's income using the time-series and cross-provincial data in 1978–2017.FindingsWhile China has experienced significant RT and ST, the levels and speeds of these transformations differed largely among provinces. Higher and faster RT and ST are often positively associated with the higher and faster growth of rural income. Based on this study, a general typology of rural and structural transformations and rural income is developed. The likely impacts of institutions, policies and investments (IPIs) on RT are discussed.Originality/valueThe authors believe that the findings of this study provide the insights on regional RT and ST and policy implications to increase farmer's income through facilitating and speeding up RT and ST with appropriate IPIs during the rural transformation.

scholarly journals Time Series Modelling of Monthly average Temperature in Gaborone-Botswana

2019 ◽  
Vol 6 (2) ◽  
pp. 689-710
Author(s):  
Keorapetse Sediakgotla ◽  
Wilford Molefe ◽  
Dahud Kehinde Shangodoyin
Keyword(s):  
Time Series ◽  
Average Temperature ◽  
Monthly Average ◽  
Monthly Average Temperature ◽  
Time Series Modelling

scholarly journals Exports, Productivity and Economic Growth in Pakistan: A Time Series Analysis

2006 ◽  
Vol 11 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Aurangzeb Aurangzeb
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Time Series Analysis ◽  
Analytical Framework ◽  
Trade Relations ◽  
Export Sector ◽  
The Past ◽  
Positive Externalities ◽  
The Difference ◽  
The Relationship

This paper investigates the relationship between exports and economic growth in Pakistan by utilizing the analytical framework put forward by Feder (1983). The hypothesis that marginal factor productivities are not equal in export and non-export sectors of the Pakistan economy is tested by using time series from 1973 to 2005. The estimation results indicate that marginal factor productivities are significantly higher in the export sector. Moreover, the difference seems to derive, in part, from inter-sectoral positive externalities generated by the export sector. In broad terms, therefore, the results of this study are supportive of the export oriented, outward-looking approach to trade relations adopted by policymakers over the past decade.

scholarly journals Monthly Average Temperature Modeling in an Intertropical Region

2013 ◽  
Vol 28 (5) ◽  
pp. 1099-1115 ◽  
Author(s):  
Mercedes Andrade-Bejarano
Keyword(s):  
Time Series ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Seasonal Pattern ◽  
Geographical Location ◽  
Average Temperature ◽  
Monthly Average ◽  
Monthly Average Temperature ◽  
Valle Del Cauca

Abstract Data for this research come from time series of monthly average temperatures from 28 sites over the Valle del Cauca of Colombia in South America, collected over the period 1971–2002. Because of the geographical location of the study area, monthly average temperature is affected by altitude and El Niño–La Niña (El Niño–Southern Oscillation, or ENSO phenomenon). Time series for some of the sites show a tendency to increase. Also, because of the two dry and wet periods in the study area, a seasonal pattern of behavior in monthly average temperature is seen. Linear mixed models are formulated and fitted to account for within- and between-site variations. The ENSO phenomenon is modeled by the Southern Oscillation index (SOI) and dummy variables. Spatial and temporal covariance structures in the errors are modeled individually using isotropic variogram models. The fitted models demonstrate the influence of the ENSO phenomenon on monthly average temperatures; this is seen in the maps produced from the models for ENSO and normal conditions. These maps show the predicted spatial patterns for differences in temperature throughout the study area.

Prediction of Financial Time-Series Signals Using á Trous Wavelet Transform

2015 ◽  
Vol 781 ◽  
pp. 523-526 ◽  
Author(s):  
Wassanun Sangjun ◽  
Supawat Supakwong ◽  
Suttipong Thajchayapong
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Polynomial Regression ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Real World Data ◽  
Financial Time ◽  
The Past ◽  
Significant Performance

This paper proposes a financial time-series prediction method consisting of á Trous wavelet transform and polynomial regression. The main purpose of employing á Trous wavelet transform is to decompose financial time-series signals into different resolutions where only relevant signal components are used for prediction. Also, á Trous wavelet transform is used to avoid the edge problem where only the past and present components of the time-series signal are taken into account. The decomposed time-series signals are then fed into the polynomial regression part to obtain predicted time-series signals. Using real-world data, performance evaluation is conducted based on total benefit and profit/loss where it is shown that á Trous wavelet transform contributes to a significant performance improvement.

scholarly journals Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Data ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 55
Author(s):  
Giuseppe Ciaburro ◽  
Gino Iannace
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Time Sequence ◽  
Series Data ◽  
Time Intervals ◽  
The Past ◽  
Future Behavior ◽  
Measurable Variable ◽  
The Future

To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data.

scholarly journals The frequency of pattern occurrence in random walks

2015 ◽  
Vol DMTCS Proceedings, 27th... (Proceedings) ◽  
Author(s):  
Sergi Elizalde ◽  
Megan Martinez
Keyword(s):  
Time Series ◽  
Random Walks ◽  
Permutation Patterns ◽  
Equal Frequency ◽  
Time Intervals ◽  
The Past ◽  
Data Points ◽  
International Audience ◽  
Ordinal Patterns ◽  
Analysis Of Time Series

International audience In the past decade, the use of ordinal patterns in the analysis of time series and dynamical systems has become an important tool. Ordinal patterns (otherwise known as a permutation patterns) are found in time series by taking $n$ data points at evenly-spaced time intervals and mapping them to a length-$n$ permutation determined by relative ordering. The frequency with which certain patterns occur is a useful statistic for such series. However, the behavior of the frequency of pattern occurrence is unstudied for most models. We look at the frequency of pattern occurrence in random walks in discrete time, and we define a natural equivalence relation on permutations under which equivalent patterns appear with equal frequency, regardless of probability distribution. We characterize these equivalence classes applying combinatorial methods. Au cours de la dernière décennie, l’utilisation des motifs ordinaux dans l’analyse des séries chronologiques et systèmes dynamiques est devenu un outil important. Des motifs ordinaux (autrement appelés motifs de permutations) se trouvent dans les séries chronologiques en prenant $n$ points de données au intervalles de temps uniformément espacées et les faisant correspondre à une permutation de longueur $n$ déterminée par leur ordre relatif. La fréquence avec laquelle certains motifs apparaissent est une statistique utile pour ces séries. Toutefois, le comportement de la fréquence d’apparition de ces motifs n’a pas été étudié pour la plupart des modèles. Nous regardons la fréquence d’occurrence des motifs dans les marches aléatoires en temps discret, et nous définissons une relation d’équivalence naturelle sur des permutations dans laquelle les motifs équivalents apparaissent avec la même fréquence, quelle que soit la distribution de probabilité. Nous caractérisons ces classes d’équivalence utilisant des méthodes combinatoires

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