Find-out abnormal behavior over the timeHow to train model to predict events 30 minutes prior, from multi-dimensionnal timeseriesUsing time series data from a sensor for MLSales Predictions Over TimeServer log analysis using machine learningPredicting or patron find of a binary variable over timeTo detect unauthorized access using outlier detectionHow to find out the percentage of contribution of a variable for another variable/feature?Time Series Autocorrelation EstimationHow to find similarity of two series over time containing periodic trends?

Optimising a list searching algorithm

Generic TVP tradeoffs?

How does one measure the Fourier components of a signal?

Pronounciation of the combination "st" in spanish accents

Print last inputted byte

Bash - pair each line of file

Why is there so much iron?

Usage and meaning of "up" in "...worth at least a thousand pounds up in London"

Knife as defense against stray dogs

How to terminate ping <dest> &

In Aliens, how many people were on LV-426 before the Marines arrived​?

How to define limit operations in general topological spaces? Are nets able to do this?

Hausdorff dimension of the boundary of fibres of Lipschitz maps

Do US professors/group leaders only get a salary, but no group budget?

Can other pieces capture a threatening piece and prevent a checkmate?

World War I as a war of liberals against authoritarians?

What does Jesus mean regarding "Raca," and "you fool?" - is he contrasting them?

What are substitutions for coconut in curry?

Can you move over difficult terrain with only 5 feet of movement?

Using Past-Perfect interchangeably with the Past Continuous

Light propagating through a sound wave

Wrapping homogeneous Python objects

In what cases must I use 了 and in what cases not?

Describing a chess game in a novel



Find-out abnormal behavior over the time


How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseriesUsing time series data from a sensor for MLSales Predictions Over TimeServer log analysis using machine learningPredicting or patron find of a binary variable over timeTo detect unauthorized access using outlier detectionHow to find out the percentage of contribution of a variable for another variable/feature?Time Series Autocorrelation EstimationHow to find similarity of two series over time containing periodic trends?













0












$begingroup$


I want to detect abnormal behaviour in a oil pipe where the oil is flowing with some constant pressure. I have a sensor which monitors and pressure over the time and push it to my cloud server.



I got the dataset . My requirement is to do data analytics in python and find-out the abnormal pattern in the dataset over the time. And I need to suggest abnormal behaviors present in the dataset ., say for example., from 1 pm to 3:30 pm today , the pressure raises and falls may be due to some leakage in the pipe.



can we do it by simple statistical model? or machine learning is required?



Can please suggest the best suitable machine learning algorithm for this scenario?



Also please mention the web links over here.



Thanks










share|improve this question









$endgroup$







  • 1




    $begingroup$
    Visit (sections of) the pipeline. Talk to the people who maintain it. There is only so much that you can see from data. You never know if someone is digging near the pipe that might cause fluctuations in the data which are not caused by leakage.
    $endgroup$
    – phiver
    Feb 13 '18 at 14:12















0












$begingroup$


I want to detect abnormal behaviour in a oil pipe where the oil is flowing with some constant pressure. I have a sensor which monitors and pressure over the time and push it to my cloud server.



I got the dataset . My requirement is to do data analytics in python and find-out the abnormal pattern in the dataset over the time. And I need to suggest abnormal behaviors present in the dataset ., say for example., from 1 pm to 3:30 pm today , the pressure raises and falls may be due to some leakage in the pipe.



can we do it by simple statistical model? or machine learning is required?



Can please suggest the best suitable machine learning algorithm for this scenario?



Also please mention the web links over here.



Thanks










share|improve this question









$endgroup$







  • 1




    $begingroup$
    Visit (sections of) the pipeline. Talk to the people who maintain it. There is only so much that you can see from data. You never know if someone is digging near the pipe that might cause fluctuations in the data which are not caused by leakage.
    $endgroup$
    – phiver
    Feb 13 '18 at 14:12













0












0








0





$begingroup$


I want to detect abnormal behaviour in a oil pipe where the oil is flowing with some constant pressure. I have a sensor which monitors and pressure over the time and push it to my cloud server.



I got the dataset . My requirement is to do data analytics in python and find-out the abnormal pattern in the dataset over the time. And I need to suggest abnormal behaviors present in the dataset ., say for example., from 1 pm to 3:30 pm today , the pressure raises and falls may be due to some leakage in the pipe.



can we do it by simple statistical model? or machine learning is required?



Can please suggest the best suitable machine learning algorithm for this scenario?



Also please mention the web links over here.



Thanks










share|improve this question









$endgroup$




I want to detect abnormal behaviour in a oil pipe where the oil is flowing with some constant pressure. I have a sensor which monitors and pressure over the time and push it to my cloud server.



I got the dataset . My requirement is to do data analytics in python and find-out the abnormal pattern in the dataset over the time. And I need to suggest abnormal behaviors present in the dataset ., say for example., from 1 pm to 3:30 pm today , the pressure raises and falls may be due to some leakage in the pipe.



can we do it by simple statistical model? or machine learning is required?



Can please suggest the best suitable machine learning algorithm for this scenario?



Also please mention the web links over here.



Thanks







machine-learning dataset time-series statistics probability






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Feb 13 '18 at 6:29









JavaUserJavaUser

1011




1011







  • 1




    $begingroup$
    Visit (sections of) the pipeline. Talk to the people who maintain it. There is only so much that you can see from data. You never know if someone is digging near the pipe that might cause fluctuations in the data which are not caused by leakage.
    $endgroup$
    – phiver
    Feb 13 '18 at 14:12












  • 1




    $begingroup$
    Visit (sections of) the pipeline. Talk to the people who maintain it. There is only so much that you can see from data. You never know if someone is digging near the pipe that might cause fluctuations in the data which are not caused by leakage.
    $endgroup$
    – phiver
    Feb 13 '18 at 14:12







1




1




$begingroup$
Visit (sections of) the pipeline. Talk to the people who maintain it. There is only so much that you can see from data. You never know if someone is digging near the pipe that might cause fluctuations in the data which are not caused by leakage.
$endgroup$
– phiver
Feb 13 '18 at 14:12




$begingroup$
Visit (sections of) the pipeline. Talk to the people who maintain it. There is only so much that you can see from data. You never know if someone is digging near the pipe that might cause fluctuations in the data which are not caused by leakage.
$endgroup$
– phiver
Feb 13 '18 at 14:12










1 Answer
1






active

oldest

votes


















2












$begingroup$

Your problem definition



You have time series data which is used to measure the pressure using your sensor. You wish to identify when the pressure recordings are abnormal. This problem would be best solved using anomaly detection algorithms. But, there are so many ways that you can approach this problem.



I would use a sliding window approach and use that as your feature space to detect the distribution of your recordings. The window length you will select $m$ is the first of your hyper-parameters that you will need to tune.



Using a statistical model



The retained time series $X in mathbbR^m$, you can treat this signal as a queue, first in first out. When a new recording is defined then discard the oldest datapoint. For a given set $X$ of samples, get the mean and the standard deviation $sigma$. If the new point exceeds a multiple of $sigma$, usually this is set to $3sigma$, but it depends on the expected variation of your sensor, then we flag a state change.



You can further extend this by using the generalized likelihood ratio test (GLRT) to determine when a new sample causes the point to fall significantly outside the distribution of the null hypothesis. In which case this indicates a state change (the new point came from a different distribution than the normal flow through the pipe).



Using machine learning



You can collect multiple instances of your time series and annotate them based on your experience as being nominal or anomalous. Then you will have a supervised 2-class classification problem. First you should attempt some feature extraction using PCA, LDA or similar techniques.. Then you can attempt to use all the fun algorithms available to you through scikit-learn (SVM, Random Forests, K-NN, etc.).



If there is a significantly higher number of nominal instances than anomalous ones, this will introduce bias to your model. Anomaly detection algorithms are better suited for these types of problems. These algorithms learn the distribution within which your nominal set should belong. Then for novel instances it evaluates the probability of it being contained in the learned distribution. If the probability is small, then the algorithm will flag the instance as anomalous.



For more information on anomaly detection for time series refer to:



Using time series data from a sensor for ML



How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries






share|improve this answer









$endgroup$












    Your Answer





    StackExchange.ifUsing("editor", function ()
    return StackExchange.using("mathjaxEditing", function ()
    StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
    StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
    );
    );
    , "mathjax-editing");

    StackExchange.ready(function()
    var channelOptions =
    tags: "".split(" "),
    id: "557"
    ;
    initTagRenderer("".split(" "), "".split(" "), channelOptions);

    StackExchange.using("externalEditor", function()
    // Have to fire editor after snippets, if snippets enabled
    if (StackExchange.settings.snippets.snippetsEnabled)
    StackExchange.using("snippets", function()
    createEditor();
    );

    else
    createEditor();

    );

    function createEditor()
    StackExchange.prepareEditor(
    heartbeatType: 'answer',
    autoActivateHeartbeat: false,
    convertImagesToLinks: false,
    noModals: true,
    showLowRepImageUploadWarning: true,
    reputationToPostImages: null,
    bindNavPrevention: true,
    postfix: "",
    imageUploader:
    brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
    contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
    allowUrls: true
    ,
    onDemand: true,
    discardSelector: ".discard-answer"
    ,immediatelyShowMarkdownHelp:true
    );



    );













    draft saved

    draft discarded


















    StackExchange.ready(
    function ()
    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f27753%2ffind-out-abnormal-behavior-over-the-time%23new-answer', 'question_page');

    );

    Post as a guest















    Required, but never shown

























    1 Answer
    1






    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    2












    $begingroup$

    Your problem definition



    You have time series data which is used to measure the pressure using your sensor. You wish to identify when the pressure recordings are abnormal. This problem would be best solved using anomaly detection algorithms. But, there are so many ways that you can approach this problem.



    I would use a sliding window approach and use that as your feature space to detect the distribution of your recordings. The window length you will select $m$ is the first of your hyper-parameters that you will need to tune.



    Using a statistical model



    The retained time series $X in mathbbR^m$, you can treat this signal as a queue, first in first out. When a new recording is defined then discard the oldest datapoint. For a given set $X$ of samples, get the mean and the standard deviation $sigma$. If the new point exceeds a multiple of $sigma$, usually this is set to $3sigma$, but it depends on the expected variation of your sensor, then we flag a state change.



    You can further extend this by using the generalized likelihood ratio test (GLRT) to determine when a new sample causes the point to fall significantly outside the distribution of the null hypothesis. In which case this indicates a state change (the new point came from a different distribution than the normal flow through the pipe).



    Using machine learning



    You can collect multiple instances of your time series and annotate them based on your experience as being nominal or anomalous. Then you will have a supervised 2-class classification problem. First you should attempt some feature extraction using PCA, LDA or similar techniques.. Then you can attempt to use all the fun algorithms available to you through scikit-learn (SVM, Random Forests, K-NN, etc.).



    If there is a significantly higher number of nominal instances than anomalous ones, this will introduce bias to your model. Anomaly detection algorithms are better suited for these types of problems. These algorithms learn the distribution within which your nominal set should belong. Then for novel instances it evaluates the probability of it being contained in the learned distribution. If the probability is small, then the algorithm will flag the instance as anomalous.



    For more information on anomaly detection for time series refer to:



    Using time series data from a sensor for ML



    How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries






    share|improve this answer









    $endgroup$

















      2












      $begingroup$

      Your problem definition



      You have time series data which is used to measure the pressure using your sensor. You wish to identify when the pressure recordings are abnormal. This problem would be best solved using anomaly detection algorithms. But, there are so many ways that you can approach this problem.



      I would use a sliding window approach and use that as your feature space to detect the distribution of your recordings. The window length you will select $m$ is the first of your hyper-parameters that you will need to tune.



      Using a statistical model



      The retained time series $X in mathbbR^m$, you can treat this signal as a queue, first in first out. When a new recording is defined then discard the oldest datapoint. For a given set $X$ of samples, get the mean and the standard deviation $sigma$. If the new point exceeds a multiple of $sigma$, usually this is set to $3sigma$, but it depends on the expected variation of your sensor, then we flag a state change.



      You can further extend this by using the generalized likelihood ratio test (GLRT) to determine when a new sample causes the point to fall significantly outside the distribution of the null hypothesis. In which case this indicates a state change (the new point came from a different distribution than the normal flow through the pipe).



      Using machine learning



      You can collect multiple instances of your time series and annotate them based on your experience as being nominal or anomalous. Then you will have a supervised 2-class classification problem. First you should attempt some feature extraction using PCA, LDA or similar techniques.. Then you can attempt to use all the fun algorithms available to you through scikit-learn (SVM, Random Forests, K-NN, etc.).



      If there is a significantly higher number of nominal instances than anomalous ones, this will introduce bias to your model. Anomaly detection algorithms are better suited for these types of problems. These algorithms learn the distribution within which your nominal set should belong. Then for novel instances it evaluates the probability of it being contained in the learned distribution. If the probability is small, then the algorithm will flag the instance as anomalous.



      For more information on anomaly detection for time series refer to:



      Using time series data from a sensor for ML



      How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries






      share|improve this answer









      $endgroup$















        2












        2








        2





        $begingroup$

        Your problem definition



        You have time series data which is used to measure the pressure using your sensor. You wish to identify when the pressure recordings are abnormal. This problem would be best solved using anomaly detection algorithms. But, there are so many ways that you can approach this problem.



        I would use a sliding window approach and use that as your feature space to detect the distribution of your recordings. The window length you will select $m$ is the first of your hyper-parameters that you will need to tune.



        Using a statistical model



        The retained time series $X in mathbbR^m$, you can treat this signal as a queue, first in first out. When a new recording is defined then discard the oldest datapoint. For a given set $X$ of samples, get the mean and the standard deviation $sigma$. If the new point exceeds a multiple of $sigma$, usually this is set to $3sigma$, but it depends on the expected variation of your sensor, then we flag a state change.



        You can further extend this by using the generalized likelihood ratio test (GLRT) to determine when a new sample causes the point to fall significantly outside the distribution of the null hypothesis. In which case this indicates a state change (the new point came from a different distribution than the normal flow through the pipe).



        Using machine learning



        You can collect multiple instances of your time series and annotate them based on your experience as being nominal or anomalous. Then you will have a supervised 2-class classification problem. First you should attempt some feature extraction using PCA, LDA or similar techniques.. Then you can attempt to use all the fun algorithms available to you through scikit-learn (SVM, Random Forests, K-NN, etc.).



        If there is a significantly higher number of nominal instances than anomalous ones, this will introduce bias to your model. Anomaly detection algorithms are better suited for these types of problems. These algorithms learn the distribution within which your nominal set should belong. Then for novel instances it evaluates the probability of it being contained in the learned distribution. If the probability is small, then the algorithm will flag the instance as anomalous.



        For more information on anomaly detection for time series refer to:



        Using time series data from a sensor for ML



        How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries






        share|improve this answer









        $endgroup$



        Your problem definition



        You have time series data which is used to measure the pressure using your sensor. You wish to identify when the pressure recordings are abnormal. This problem would be best solved using anomaly detection algorithms. But, there are so many ways that you can approach this problem.



        I would use a sliding window approach and use that as your feature space to detect the distribution of your recordings. The window length you will select $m$ is the first of your hyper-parameters that you will need to tune.



        Using a statistical model



        The retained time series $X in mathbbR^m$, you can treat this signal as a queue, first in first out. When a new recording is defined then discard the oldest datapoint. For a given set $X$ of samples, get the mean and the standard deviation $sigma$. If the new point exceeds a multiple of $sigma$, usually this is set to $3sigma$, but it depends on the expected variation of your sensor, then we flag a state change.



        You can further extend this by using the generalized likelihood ratio test (GLRT) to determine when a new sample causes the point to fall significantly outside the distribution of the null hypothesis. In which case this indicates a state change (the new point came from a different distribution than the normal flow through the pipe).



        Using machine learning



        You can collect multiple instances of your time series and annotate them based on your experience as being nominal or anomalous. Then you will have a supervised 2-class classification problem. First you should attempt some feature extraction using PCA, LDA or similar techniques.. Then you can attempt to use all the fun algorithms available to you through scikit-learn (SVM, Random Forests, K-NN, etc.).



        If there is a significantly higher number of nominal instances than anomalous ones, this will introduce bias to your model. Anomaly detection algorithms are better suited for these types of problems. These algorithms learn the distribution within which your nominal set should belong. Then for novel instances it evaluates the probability of it being contained in the learned distribution. If the probability is small, then the algorithm will flag the instance as anomalous.



        For more information on anomaly detection for time series refer to:



        Using time series data from a sensor for ML



        How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Feb 13 '18 at 7:11









        JahKnowsJahKnows

        5,137625




        5,137625



























            draft saved

            draft discarded
















































            Thanks for contributing an answer to Data Science Stack Exchange!


            • Please be sure to answer the question. Provide details and share your research!

            But avoid


            • Asking for help, clarification, or responding to other answers.

            • Making statements based on opinion; back them up with references or personal experience.

            Use MathJax to format equations. MathJax reference.


            To learn more, see our tips on writing great answers.




            draft saved


            draft discarded














            StackExchange.ready(
            function ()
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f27753%2ffind-out-abnormal-behavior-over-the-time%23new-answer', 'question_page');

            );

            Post as a guest















            Required, but never shown





















































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown

































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown







            Popular posts from this blog

            Францішак Багушэвіч Змест Сям'я | Біяграфія | Творчасць | Мова Багушэвіча | Ацэнкі дзейнасці | Цікавыя факты | Спадчына | Выбраная бібліяграфія | Ушанаванне памяці | У філатэліі | Зноскі | Літаратура | Спасылкі | НавігацыяЛяхоўскі У. Рупіўся дзеля Бога і людзей: Жыццёвы шлях Лявона Вітан-Дубейкаўскага // Вольскі і Памідораў з песняй пра немца Адвакат, паэт, народны заступнік Ашмянскі веснікВ Минске появится площадь Богушевича и улица Сырокомли, Белорусская деловая газета, 19 июля 2001 г.Айцец беларускай нацыянальнай ідэі паўстаў у бронзе Сяргей Аляксандравіч Адашкевіч (1918, Мінск). 80-я гады. Бюст «Францішак Багушэвіч».Яўген Мікалаевіч Ціхановіч. «Партрэт Францішка Багушэвіча»Мікола Мікалаевіч Купава. «Партрэт зачынальніка новай беларускай літаратуры Францішка Багушэвіча»Уладзімір Іванавіч Мелехаў. На помніку «Змагарам за родную мову» Барэльеф «Францішак Багушэвіч»Памяць пра Багушэвіча на Віленшчыне Страчаная сталіца. Беларускія шыльды на вуліцах Вільні«Krynica». Ideologia i przywódcy białoruskiego katolicyzmuФранцішак БагушэвічТворы на knihi.comТворы Францішка Багушэвіча на bellib.byСодаль Уладзімір. Францішак Багушэвіч на Лідчыне;Луцкевіч Антон. Жыцьцё і творчасьць Фр. Багушэвіча ў успамінах ягоных сучасьнікаў // Запісы Беларускага Навуковага таварыства. Вільня, 1938. Сшытак 1. С. 16-34.Большая российская1188761710000 0000 5537 633Xn9209310021619551927869394п

            Беларусь Змест Назва Гісторыя Геаграфія Сімволіка Дзяржаўны лад Палітычныя партыі Міжнароднае становішча і знешняя палітыка Адміністрацыйны падзел Насельніцтва Эканоміка Культура і грамадства Сацыяльная сфера Узброеныя сілы Заўвагі Літаратура Спасылкі НавігацыяHGЯOiТоп-2011 г. (па версіі ej.by)Топ-2013 г. (па версіі ej.by)Топ-2016 г. (па версіі ej.by)Топ-2017 г. (па версіі ej.by)Нацыянальны статыстычны камітэт Рэспублікі БеларусьШчыльнасць насельніцтва па краінахhttp://naviny.by/rubrics/society/2011/09/16/ic_articles_116_175144/А. Калечыц, У. Ксяндзоў. Спробы засялення краю неандэртальскім чалавекам.І ў Менску былі мамантыА. Калечыц, У. Ксяндзоў. Старажытны каменны век (палеаліт). Першапачатковае засяленне тэрыторыіГ. Штыхаў. Балты і славяне ў VI—VIII стст.М. Клімаў. Полацкае княства ў IX—XI стст.Г. Штыхаў, В. Ляўко. Палітычная гісторыя Полацкай зямліГ. Штыхаў. Дзяржаўны лад у землях-княствахГ. Штыхаў. Дзяржаўны лад у землях-княствахБеларускія землі ў складзе Вялікага Княства ЛітоўскагаЛюблінская унія 1569 г."The Early Stages of Independence"Zapomniane prawdy25 гадоў таму было аб'яўлена, што Язэп Пілсудскі — беларус (фота)Наша вадаДакументы ЧАЭС: Забруджванне тэрыторыі Беларусі « ЧАЭС Зона адчужэнняСведения о политических партиях, зарегистрированных в Республике Беларусь // Министерство юстиции Республики БеларусьСтатыстычны бюлетэнь „Полаўзроставая структура насельніцтва Рэспублікі Беларусь на 1 студзеня 2012 года і сярэднегадовая колькасць насельніцтва за 2011 год“Индекс человеческого развития Беларуси — не было бы нижеБеларусь занимает первое место в СНГ по индексу развития с учетом гендерного факцёраНацыянальны статыстычны камітэт Рэспублікі БеларусьКанстытуцыя РБ. Артыкул 17Трансфармацыйныя задачы БеларусіВыйсце з крызісу — далейшае рэфармаванне Беларускі рубель — сусветны лідар па дэвальвацыяхПра змену коштаў у кастрычніку 2011 г.Бядней за беларусаў у СНД толькі таджыкіСярэдні заробак у верасні дасягнуў 2,26 мільёна рублёўЭканомікаГаласуем за ТОП-100 беларускай прозыСучасныя беларускія мастакіАрхитектура Беларуси BELARUS.BYА. Каханоўскі. Культура Беларусі ўсярэдзіне XVII—XVIII ст.Анталогія беларускай народнай песні, гуказапісы спеваўБеларускія Музычныя IнструментыБеларускі рок, які мы страцілі. Топ-10 гуртоў«Мясцовы час» — нязгаслая легенда беларускай рок-музыкіСЯРГЕЙ БУДКІН. МЫ НЯ ЗНАЕМ СВАЁЙ МУЗЫКІМ. А. Каладзінскі. НАРОДНЫ ТЭАТРМагнацкія культурныя цэнтрыПублічная дыскусія «Беларуская новая пьеса: без беларускай мовы ці беларуская?»Беларускія драматургі па-ранейшаму лепш ставяцца за мяжой, чым на радзіме«Працэс незалежнага кіно пайшоў, і дзяржаву турбуе яго непадкантрольнасць»Беларускія філосафы ў пошуках прасторыВсе идём в библиотекуАрхіваванаАб Нацыянальнай праграме даследавання і выкарыстання касмічнай прасторы ў мірных мэтах на 2008—2012 гадыУ космас — разам.У суседнім з Барысаўскім раёне пабудуюць Камандна-вымяральны пунктСвяты і абрады беларусаў«Мірныя бульбашы з малой краіны» — 5 непраўдзівых стэрэатыпаў пра БеларусьМ. Раманюк. Беларускае народнае адзеннеУ Беларусі скарачаецца колькасць злачынстваўЛукашэнка незадаволены мінскімі ўладамі Крадзяжы складаюць у Мінску каля 70% злачынстваў Узровень злачыннасці ў Мінскай вобласці — адзін з самых высокіх у краіне Генпракуратура аналізуе стан са злачыннасцю ў Беларусі па каэфіцыенце злачыннасці У Беларусі стабілізавалася крымінагеннае становішча, лічыць генпракурорЗамежнікі сталі здзяйсняць у Беларусі больш злачынстваўМУС Беларусі турбуе рост рэцыдыўнай злачыннасціЯ з ЖЭСа. Дазволіце вас абкрасці! Рэйтынг усіх службаў і падраздзяленняў ГУУС Мінгарвыканкама вырасАб КДБ РБГісторыя Аператыўна-аналітычнага цэнтра РБГісторыя ДКФРТаможняagentura.ruБеларусьBelarus.by — Афіцыйны сайт Рэспублікі БеларусьСайт урада БеларусіRadzima.org — Збор архітэктурных помнікаў, гісторыя Беларусі«Глобус Беларуси»Гербы и флаги БеларусиАсаблівасці каменнага веку на БеларусіА. Калечыц, У. Ксяндзоў. Старажытны каменны век (палеаліт). Першапачатковае засяленне тэрыторыіУ. Ксяндзоў. Сярэдні каменны век (мезаліт). Засяленне краю плямёнамі паляўнічых, рыбакоў і збіральнікаўА. Калечыц, М. Чарняўскі. Плямёны на тэрыторыі Беларусі ў новым каменным веку (неаліце)А. Калечыц, У. Ксяндзоў, М. Чарняўскі. Гаспадарчыя заняткі ў каменным векуЭ. Зайкоўскі. Духоўная культура ў каменным векуАсаблівасці бронзавага веку на БеларусіФарміраванне супольнасцей ранняга перыяду бронзавага векуФотографии БеларусиРоля беларускіх зямель ва ўтварэнні і ўмацаванні ВКЛВ. Фадзеева. З гісторыі развіцця беларускай народнай вышыўкіDMOZGran catalanaБольшая российскаяBritannica (анлайн)Швейцарскі гістарычны15325917611952699xDA123282154079143-90000 0001 2171 2080n9112870100577502ge128882171858027501086026362074122714179пппппп

            Герб Смалявічаў Апісанне | Спасылкі | НавігацыяГерб города Смолевичип