Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

AbstractThe objective of this paper is to determine the trend and to estimate the streamflow of the Gökırmak River. The possible trend of the streamflow was forecasted using an autoregressive integrated moving average (ARIMA) model. Time series and trend analyses were performed using monthly streamflow data for the period between 1999 and 2014. Pettitt’s change point analysis was employed to detect the time of change for historical streamflow time series. Kendall’s tau and Spearman’s rho tests were also conducted. The results of the change point analysis determined the change point as 2008. The time series analysis showed that the streamflow of the river had a decreasing trend from the past to the present. Results of the trend analysis forecasted a decreasing trend for the streamflow in the future. The decreasing trend in the streamflow may be related to climate change. This paper provides preliminary knowledge of the streamflow trend for the Gökırmak River.

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Climate change effects on annual streamflow of Filyos River (Turkey)

Journal of Water and Climate Change ◽

10.2166/wcc.2018.060 ◽

2018 ◽

Vol 11 (2) ◽

pp. 420-433 ◽

Cited By ~ 6

Author(s):

Adem Yavuz Sönmez ◽

Semih Kale

Keyword(s):

Climate Change ◽

Trend Analysis ◽

Change Point ◽

Change Point Analysis ◽

Climatic Data ◽

Climatic Parameters ◽

Annual Streamflow ◽

Climate Change Effects ◽

Temperature And Precipitation ◽

Point Analysis

Abstract The main purpose of this study was to estimate possible climate change effects on the annual streamflow of Filyos River (Turkey). Data for annual streamflow and climatic parameters were obtained from streamflow gauging stations on the river and Bartın, Karabük, Zonguldak meteorological observation stations. Time series analysis was performed on 46 years of annual streamflow data and 57 years of annual mean climatic data from three monitoring stations to understand the trends. Pettitt change-point analysis was applied to determine the change time and trend analysis was performed to forecast trends. To reveal the relationship between climatic parameters and streamflow, correlation tests, namely, Spearman's rho and Kendall's tau were applied. The results of Pettitt change-point analysis pointed to 2000 as the change year for streamflow. Change years for temperature and precipitation were detected as 1997 and 2000, respectively. Trend analysis results indicated decreasing trends in the streamflow and precipitation, and increasing trend in temperature. These changes were found statistically significant for streamflow (p < 0.05) and temperature (p < 0.01). Also, a statistically significant (p < 0.05) correlation was found between streamflow and precipitation. In conclusion, decreasing precipitation and increasing temperature as a result of climate change initiated a decrease in the river streamflow.

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Reduction in hospitalisations for acute gastroenteritis-associated childhood seizures since introduction of rotavirus vaccination: a time-series and change-point analysis of hospital admissions in England

Journal of Epidemiology & Community Health ◽

10.1136/jech-2019-213055 ◽

2019 ◽

Vol 73 (11) ◽

pp. 1020-1025 ◽

Cited By ~ 4

Author(s):

Daniel James Hungerford ◽

Neil French ◽

Miren Iturriza-Gómara ◽

Jonathan M Read ◽

Nigel A Cunliffe ◽

...

Keyword(s):

Time Series ◽

Acute Gastroenteritis ◽

Change Point ◽

Hospital Admissions ◽

Population Level ◽

Febrile Seizures ◽

Change Point Analysis ◽

Rotavirus Vaccination ◽

Point Analysis ◽

The Impact

IntroductionThe incidence of severe childhood diarrhoea has fallen substantially following the introduction of rotavirus vaccine in the UK in July 2013. Since children with rotavirus infection may experience febrile and afebrile seizures, we evaluated the impact of rotavirus vaccination on seizure hospitalisations in children in England.MethodsUsing data from Hospital Episode Statistics, we employed interrupted time-series analyses to assess changes in monthly hospital admissions for seizures among children aged <5 years from July 2000 to June 2017. Outcome measures comprised all seizures and febrile seizures, with and without a co-diagnosis of acute gastroenteritis (AGE). Models were adjusted for pneumococcal conjugate vaccine (PCV) introduction. Change-point analysis was used to independently identify step-changes in the time-series.ResultsAmong hospitalised children aged <5 years, the incidence of any seizures and febrile seizures with AGE decreased post-vaccine introduction by 23% (95% CI: 11% to 33%) and 31% (95% CI: 19% to 41%), respectively. For febrile seizures with AGE, a single change-point was identified in July 2013 (95% CI: June 2013 to December 2013). Reductions in seizure incidence were higher during the rotavirus season (49%, 95% CI: 37% to 58%) compared with out-of-season (13%, 95% CI: −4 to 28%) and showed no relation to PCV introduction. There were small reductions in any seizures with any co-diagnosis (4%, 95% CI: 0% to 8%) and in febrile seizures with any co-diagnosis (10%, 95% CI: 2% to 16%).ConclusionRotavirus vaccination has reduced hospitalisations for seizures associated with AGE in England, providing additional evidence of population-level impact of rotavirus vaccination on seizure incidence in high-income countries.

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Prediksi Pencurian Sepeda Motor Menggunakan Model Time Series (Studi Kasus: Polres Kotabumi Lampung Utara)

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol14iss3pp425-434 ◽

2020 ◽

Vol 14 (3) ◽

pp. 425-434

Author(s):

MELI PRANATA ◽

DIAN ANGGRAINI ◽

Deden Makbuloh ◽

Achi Rinaldi

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Time Series Model ◽

Ar Model ◽

Autoregressive Integrated Moving Average ◽

Model Time Series

Tindak kriminal adalah kejahatan yang melanggar undang-undang suatu Negara atau melanggar norma yang berlaku dalam masyarakat. Pencurian merupakan salah satu bentuk dari perbuatan tindak kriminal. Dampak yang ditimbulkan dari adanya pencurian adalah perasaan kurang aman, takut, dan tenang. Salah satu model yang digunakan untuk memprediksi jumlah kasus pencurian yaitu model time series. Model time series adalah serangkaian nilai pengamatan yang diambil selama periode waktu tertentu. Pada umumnya, dalam interval-interval yang sama panjang, (Spuege & Stephens, 2004). Penelitian ini bertujuan memodelkan data tindak kriminal yang terjadi di Lampung Utara dengan model Autoregressive (AR), Moving Average (MA), dan Autoregressive Integrated Moving Average (ARIMA). Selanjutnya dari model terbaik akan digunakan untuk peramalan 6 bulan kedepan. Hasil penelitian model AR , model AR , model MA , ARIMA , dan model ARIMA . Model MA memiliki koefisien parameter yang signifikan, memenuhi uji diagnostic tidak adanya residual pada model dan memiliki nilai RMSE dan AIC terkecil dengan nilai RMSE sebesar dan nilai AIC sebesar . Hasil prediksi model MA untuk 6 bulan ke depan cenderung mendatar.

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Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment (Preprint)

10.2196/preprints.11357 ◽

2018 ◽

Author(s):

Amir Talaei-Khoei ◽

James M Wilson ◽

Seyed-Farzan Kazemi

Keyword(s):

Time Series ◽

Change Point ◽

Influenza A ◽

Time Series Prediction ◽

Viral Meningitis ◽

Change Point Analysis ◽

Northern Nevada ◽

Point Analysis ◽

Syncytial Virus ◽

And Performance

BACKGROUND The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. OBJECTIVE This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. METHODS The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. RESULTS Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. CONCLUSIONS The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.

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Change-point analysis ofVP/VSratio time-series using a trans-dimensional McMC algorithm: applied to the Alto Tiberina Near Fault Observatory seismic network (Northern Apennines, Italy)

Geophysical Journal International ◽

10.1093/gji/ggz078 ◽

2019 ◽

Vol 217 (2) ◽

pp. 1217-1231 ◽

Cited By ~ 2

Author(s):

Giulio Poggiali ◽

Lauro Chiaraluce ◽

Raffaele Di Stefano ◽

Nicola Piana Agostinetti

Keyword(s):

Time Series ◽

Change Point ◽

Northern Apennines ◽

Seismic Network ◽

Change Point Analysis ◽

Mcmc Algorithm ◽

Near Fault ◽

Point Analysis

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Bayesian change-point analysis in hydrometeorological time series. Part 1. The normal model revisited

Journal of Hydrology ◽

10.1016/s0022-1694(00)00270-5 ◽

2000 ◽

Vol 235 (3-4) ◽

pp. 221-241 ◽

Cited By ~ 111

Author(s):

L. Perreault ◽

J. Bernier ◽

B. Bobée ◽

E. Parent

Keyword(s):

Time Series ◽

Change Point ◽

Change Point Analysis ◽

Normal Model ◽

Point Analysis

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Change point analysis of imprecise time series

Fuzzy Sets and Systems ◽

10.1016/j.fss.2013.03.001 ◽

2013 ◽

Vol 225 ◽

pp. 23-38 ◽

Cited By ~ 12

Author(s):

Carmela Cappelli ◽

Pierpaolo D’Urso ◽

Francesca Di Iorio

Keyword(s):

Time Series ◽

Change Point ◽

Change Point Analysis ◽

Point Analysis

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Change-Point Analysis of Tropical Night Occurrences for Five Major Cities in Republic of Korea

Advances in Meteorology ◽

10.1155/2015/801981 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Myoung-Seok Suh ◽

Chansoo Kim

Keyword(s):

Time Series ◽

Change Point ◽

Time Series Data ◽

Republic Of Korea ◽

Change Point Analysis ◽

Series Data ◽

Daily Minimum Temperature ◽

Rapid Urbanization ◽

Tropical Night ◽

Point Analysis

Bayesian change-point analysis is applied to detect a change-point in the occurrences of tropical night (TN) days in the 50-year time series data for five major cities in Republic of Korea. A TN day is simply defined as a day when the daily minimum temperature is greater than 25∘C. A Bayesian analysis is performed for detecting a change-point at an unknown time point in the TN day frequency time series, which is modeled by an independent Poisson random variable. The results showed that a single change occurred around 1993 for three cities (Seoul, Incheon, and Daegu). However, when we excluded the extraordinary year, 1994, a single change occurred around 1993 only in Seoul and Daegu. The average number of TN days in Seoul and Daegu increased significantly, by more than 150%, after the change-point year. The abrupt increase in TN day frequency in two cities over Republic of Korea around 1993 may be related to the significant decadal change in the East Asian summer monsoon around the mid 1990s and to rapid urbanization.

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Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Information ◽

10.3390/info12070274 ◽

2021 ◽

Vol 12 (7) ◽

pp. 274

Author(s):

Ourania Theodosiadou ◽

Kyriaki Pantelidou ◽

Nikolaos Bastas ◽

Despoina Chatzakou ◽

Theodora Tsikrika ◽

...

Keyword(s):

Time Series ◽

Change Point ◽

Hate Speech ◽

Change Point Detection ◽

Change Points ◽

Change Point Analysis ◽

Point Analysis ◽

Depth Analysis ◽

Textual Data ◽

Point Detection

Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness.

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