scholarly journals Road Traffic Offences in Nigeria: An Empirical Analysis using Buys-Ballot Approach

2021 ◽  
pp. 68-78
Author(s):  
K. C. N. Dozie ◽  
C.C Ibebuogu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Road Traffic ◽  
Study Data ◽  
Suitable Model ◽  
Upward Trend ◽  
Trend Pattern ◽  
Standard Deviations ◽  
Seasonal Indices

Road traffic offences in time series analysis when trend-cycle component is quadratic is discussed in this study. The study is to investigate the variance stability, trend pattern, seasonal indices and suitable model for decomposition of study data. The study shows that, the series is seasonal with evidence of upward trend or downward trend. There is an upsurge of the series in the months of March, August and November and a drop in January, June and December. The periodic standard deviations are stable while the seasonal standard deviations differ, suggesting that the series requires transformation to make the seasonal indices additive.

scholarly journals Perspectives of Decentralized Powerloom Industry in India- An Empirical Analysis

2017 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Basavraj S Kudachimath ◽  
Shashidhar S Mahantshetti
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Analysis ◽  
Human Resource ◽  
Empirical Analysis ◽  
Export Promotion ◽  
Positive Relation ◽  
Upward Trend ◽  
The World ◽  
Employment Generation

<div><p><em>Decentralized powerloom sector in Indi has always occupied a prominent place in the economic spheres of India. Present study pertains to the decentralized powerloom sector in India and its various dimensions. The data obtained from various reliable sources such as ministry of textiles, fibre2fashion, powerloom development and export promotion council and RBI reports were subjected to time series analysis and regression analysis. The results indicated there is an upward trend towards growth in terms of employment generation and production. The results pointed towards the decentralized sectors’ enormous potential to generate employment to both skilled and unskilled human resource in the country. The regression analysis showed a positive relation between the sector and GDP. In the light of the potentiality of the sector, suggestions have been put forth for harnessing the potential of the sector and make aid the country to be the most preferred source for clothing needs of the world.</em></p></div>

scholarly journals A Fuzzy Set-Valued Autoregressive Moving Average Model and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Complex Data ◽  
Arma Models ◽  
Linguistic Data ◽  
Series Analysis

Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challenging problems in time series analysis and mathematical statistics. In this study, we extended the ARMA model to the case of linguistic data that can be modeled by some symmetric fuzzy sets, and where the relations between the linguistic data of the time series can be considered as the ordinary stochastic correlation rather than fuzzy logical relations. Therefore, the concepts of set-valued or interval-valued random variables can be employed, and the notions of Aumann expectation, Fréchet variance, and covariance, as well as standardized process, were used to construct the ARMA model. We firstly determined that the estimators from the least square estimation of the ARMA (1,1) model under some L2 distance between two sets are weakly consistent. Moreover, the justified linguistic data-valued ARMA model was applied to forecast the linguistic monthly Hang Seng Index (HSI) as an empirical analysis. The obtained results from the empirical analysis indicate that the accuracy of the prediction produced from the proposed model is better than that produced from the classical one-order, two-order, three-order autoregressive (AR(1), AR(2), AR(3)) models, as well as the (1,1)-order autoregressive moving average (ARMA(1,1)) model.

scholarly journals Impact of night travel ban on road traffic crashes and fatalities in Zambia: an interrupted time series analysis

2021 ◽  
Vol 6 (12) ◽  
pp. e005481
Author(s):  
Peter Hangoma ◽  
Kantu Moonga-Mukale
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Road Traffic ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Annual Average ◽  
Traffic Crashes ◽  
Road Traffic Crashes ◽  
Series Analysis ◽  
Data Points

BackgroundThe burden of road traffic crashes (RTCs) and road traffic fatalities (RTFs) has been increasing in low-income and middle-income countries (LMICs). Most RTCs and RTFs happen at night. Although few countries, including Zambia, have implemented night travel bans, there is no evidence on the extent to which such policies may reduce crashes and fatalities.MethodsWe exploit the quasi-experimental set up afforded by the banning of night travel of public service vehicles in Zambia in 2016 and interrupted time series analysis to assess whether the ban had an impact on both levels and trends in RTCs and RTFs. We use annual administrative data for the period 2006—2020, with 10 pre-intervention and 4 post-intervention data points. In an alternative specification, we restrict the analysis to the period 2012—2020 so that the number of data points are the same pre-interventions and post-interventions. We also carry out robustness checks to rule out other possible explanation of the results including COVID-19.ResultsThe night travel ban was associated with a reduction in the level of RTCs by 4131.3 (annual average RTCs before the policy=17 668) and a reduction in the annual trend in RTCs by 2485.5. These effects were significant at below 1%, and they amount to an overall reduction in RTCs by 24%. The policy was also associated with a 57.5% reduction in RTFs. In absolute terms, the trend in RTFs reduced by 477.5 (Annual average RTFs before the policy=1124.7), which is significant at below 1% level. Our results were broadly unchanged in alternative specifications.ConclusionWe conclude that a night travel ban may be an effective way of reducing the burden of RTCs and RTFs in Zambia and other LMICs. However, complementary policies are needed to achieve more gains.

scholarly journals Nonlinear time series analysis of the fluctuations of the geomagnetic horizontal field

2002 ◽  
Vol 20 (2) ◽  
pp. 175-183 ◽  
Author(s):  
B. George ◽  
G. Renuka ◽  
K. Satheesh Kumar ◽  
C. P. Anil Kumar ◽  
C. Venugopal
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Correlation Dimension ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Suitable Model ◽  
Nonlinear Prediction ◽  
Series Analysis ◽  
Hourly Data

Abstract. A detailed nonlinear time series analysis of the hourly data of the geomagnetic horizontal intensity H measured at Kodaikanal (10.2° N; 77.5° E; mag: dip 3.5° N) has been carried out to investigate the dynamical behaviour of the fluctuations of H. The recurrence plots, spatiotemporal entropy and the result of the surrogate data test show the deterministic nature of the fluctuations, rejecting the hypothesis that H belong to the family of linear stochastic signals. The low dimensional character of the dynamics is evident from the estimated value of the correlation dimension and the fraction of false neighbours calculated for various embedding dimensions. The exponential decay of the power spectrum and the positive Lyapunov exponent indicate chaotic behaviour of the underlying dynamics of H. This is also supported by the results of the comparison of the chaotic characteristics of the time series of H with the pseudo-chaotic characteristics of coloured noise time series. We have also shown that the error involved in the short-term prediction of successive values of H, using a simple but robust, zero-order nonlinear prediction method, increases exponentially. It has also been suggested that there exists the possibility of characterizing the geomagnetic fluctuations in terms of the invariants in chaos theory, such as Lyapunov exponents and correlation dimension. The results of the analysis could also have implications in the development of a suitable model for the daily fluctuations of geomagnetic horizontal intensity.Key words. Geomagnetism and paleomagnetism (time variations, diurnal to secular) – History of geophysics (solar-planetary relationships) Magnetospheric physics (storms and substorms)

scholarly journals The impact of alcohol and road traffic policies on crash rates in Botswana, 2004–2011: A time-series analysis

2014 ◽  
Vol 70 ◽  
pp. 33-39 ◽  
Author(s):  
Miriam Sebego ◽  
Rebecca B. Naumann ◽  
Rose A. Rudd ◽  
Karen Voetsch ◽  
Ann M. Dellinger ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Road Traffic ◽  
Crash Rates ◽  
Series Analysis ◽  
The Impact

scholarly journals Assessment and prediction of road accident injuries trend using time-series models in Kurdistan

2018 ◽  
Vol 6 ◽  
Author(s):  
Maryam Parvareh ◽  
Asrin Karimi ◽  
Satar Rezaei ◽  
Abraha Woldemichael ◽  
Sairan Nili ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Road Traffic Accidents ◽  
Autoregressive Integrated Moving Average ◽  
Accident Injuries ◽  
Pedestrian Injuries

Abstract Background Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. Methods A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. Results A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants’ accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0)12, and SARIMA (1, 1, 1) (0, 0, 1)12, respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Conclusion Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.

scholarly journals Time Series Analysis of Decadal Precipitation Pattern at Selected Cities of Southern India

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
T. S. Subbiah ◽  
P. Parthiban ◽  
R. Mahesh ◽  
A. Das
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Stochastic Modeling ◽  
Tamil Nadu ◽  
Exponential Smoothing ◽  
Southern India ◽  
Precipitation Pattern ◽  
Short Term ◽  
Series Analysis

To characterize and explore the short-term climatic patterns over the last decade (Jan. 2009 to Dec. 2018), the present research has been carried out, involving time series analysis of precipitation pattern in three cities of Tamil Nadu, namely, Thanjavur, Nagapattinam, and Chennai, referring to deltaic, coastal and highly urbanized cities of Tamil Nadu, respectively. The study involves time series empirical analysis, decomposition, exponential smoothing, and various stochastic modeling. Herein, the location-specific suitable models are obtained and specific predictions are being carried out, as well.

scholarly journals Seasonality of confirmed malaria cases from 2008 to 2017 in Togo: a time series analysis by health district and target group

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Anne Thomas ◽  
Tchaa A. Bakai ◽  
Tinah Atcha-Oubou ◽  
Tchassama Tchadjobo ◽  
Nadine Bossard ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Pregnant Women ◽  
Malaria Transmission ◽  
Target Group ◽  
Health District ◽  
Malaria Control Programme ◽  
Series Analysis ◽  
Seasonal Indices ◽  
Maximum Minimum

Abstract Background This study aimed to assess the seasonality of confirmed malaria cases in Togo and to provide new indicators of malaria seasonality to the National Malaria Control Programme (NMCP). Methods Aggregated data of confirmed malaria cases were collected monthly from 2008 to 2017 by the Togo’s NMCP and stratified by health district and according to three target groups: children < 5 years old, children ≥ 5 years old and adults, and pregnant women. Time series analysis was carried out for each target group and health district. Seasonal decomposition was used to assess the seasonality of confirmed malaria cases. Maximum and minimum seasonal indices, their corresponding months, and the ratio of maximum/minimum seasonal indices reflecting the importance of malaria transmission, were provided by health district and target group. Results From 2008 to 2017, 7,951,757 malaria cases were reported in Togo. Children < 5 years old, children ≥ 5 years old and adults, and pregnant women represented 37.1%, 57.7% and 5.2% of the confirmed malaria cases, respectively. The maximum seasonal indices were observed during or shortly after a rainy season and the minimum seasonal indices during the dry season between January and April in particular. In children < 5 years old, the ratio of maximum/minimum seasonal indices was higher in the north, suggesting a higher seasonal malaria transmission, than in the south of Togo. This is also observed in the other two groups but to a lesser extent. Conclusions This study contributes to a better understanding of malaria seasonality in Togo. The indicators of malaria seasonality could allow for more accurate forecasting in malaria interventions and supply planning throughout the year.

scholarly journals Automating Time Series Analysis to Predict/Forecast Rainfall in AGUELMAM SIDI ALI Watershed in Morocco

2019 ◽  
Vol 8 (9) ◽  
pp. 3008-3014
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Scales ◽  
Univariate Analysis ◽  
Crop Productivity ◽  
Suitable Model ◽  
Estimation Of Parameters ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Series Analysis

Moroccan economy is largely based upon rainfall, use of water resources and crop productivity, for that it’s considered as an agricultural country. It’s more required and more important for any farmer to forecast rainfall prediction in order to analyze crop productivity. Predicting the atmosphere or forecasting the state of the weather is considered as challenge for scientific research. The prediction of rainfall monthly or/and seasonal time scales is the application of science and technology to invent and to schedule the agriculture strategies. Recently different research articles achieve to forecast and/or predict rainfall monthly or seasonal time scales using different techniques. The methodology followed in this work, be focused on automating time series analysis to forecast / predict precipitation daily, monthly or seasonal in Aguelmam Sidi Ali basin in Morocco for last 32 years ago from 1975 to 2007. We first have to study the rainfall data theoretically using the simplest form statistical analysis, which is the univariate analysis, as long as only one variable is involved in our case study. To get the selected and suitable model of time series to automate, we used different autocorrelation methods based on various criterion such as: Akaike Information Criterion (AIC), estimation of parameters using Yule-Walker (YW) and Maximum Likelihood Estimation (MLE). The results of our experiment show that it is possible using our system to obtain accurate rainfall prediction, with a more details and with a very fast way. It shows also that it’s possible to predict for next months or next years. To minimize the risk of floods and natural disasters within a basin in general and within the Aguelmam Sidi Ali basin in particular, accurate and timely rainfall forecasting is required.

scholarly journals The effects of tax structure on economic growth: evidence from Pakistan economies

2016 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Mahfooz Khan ◽  
Saif Ul amin ◽  
Sammandar Khan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Fiscal Policy ◽  
Time Series Analysis ◽  
Unit Root ◽  
Unit Root Test ◽  
Study Data ◽  
Causality Test ◽  
Series Analysis ◽  
Long Run

The study has been conducted to find out the effects of fiscal policy on economic growth in Pakistan. Taxes are selected as a proxy for fiscal policy and GDP as an economic growth. In this study the time series analysis was used. The study used difference tests and models. These tests were unit root test which at different levels was used for stationary and non-stationary another model was co-integration the co-integration further used two tests one was trace test and second one was maximum Eigen value these tests used for long run relationships between taxes and GDP. In this study Granger causality test lag 2 and lag 4 also for checking the effects of taxes on Pakistan GDP. The objectives of the study are to find out the relationship between taxes and GDP and also to testify the random walk between taxes and GDP. The data were taken from 1981 to 2012. Taxes dealt as an independent and GDP as a dependent variable of the study. Data were collected from Federal Bureau of Statistics and from Pakistan economic survey. Time series analysis is used to testify the hypotheses. The results of Unit Root test shows that GDP and taxes has a unit root and it is non- stationary. GDP has no unit root and stationary in nature at 1st difference level. The results of co-integration shows that both taxes and GDP no co-integration at 5 % level of significance. The study concludes that there is no Co-integration between taxes and GDP. The study recommended that fiscal policy should make according to the situation of the country and the tax rate should be change with a smooth rate.

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