NY Times: What is your opposite job? August 15, 2017
Posted by OromianEconomist in 10 best Youtube videos, 25 killer Websites that make you cleverer.Tags: The opposite job of an athlete and sport competitor, The opposite job of an economist, What Is Your Opposite Job?
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The opposite job of an economist is an agricultural grader.
Economists use these skills the most  Agricultural Graders use these skills the most 

1
Number facility 
1
Trunk strength 
2
Mathematics 
2
Handling and moving objects 
3
Mathematical reasoning 
3
Manual dexterity 
4
Systems analysis 
4
Foreign language 
5
Written expression 
5
Public safety and security 
6
Judgment and decision making 
6
Static strength 
7
Oral expression 
7
Armhand steadiness 
8
Writing 
8
Controlling machines and processes 
9
Critical thinking 
9
Finger dexterity 
10
Complex problem solving 
10
Production and processing 
Economists use these skills the least  Agricultural Graders use these skills the least 

1
Ability to focus on one sound among distractions 
1
Information ordering 
2
Depth perception 
2
Far vision 
3
Finger dexterity 
3
Pattern recognition 
4
Hearing sensitivity 
4
Near vision 
5
Visual color discrimination 
5
Making decisions and solving problems 
6
Multitasking 
6
Reading comprehension 
7
Management of material resources 
7
Active learning 
8
Management of financial resources 
8
Complex problem solving 
9
Visualization 
9
Processing information 
10
Selective attention 
10
Time management 
The opposite job of an athlete and sport competitor is an agricultural grader.
Athletes and Sports Competitors use these skills the most  Agricultural Graders use these skills the most 

1
Explosive strength 
1
Trunk strength 
2
Dynamic strength 
2
Handling and moving objects 
3
Stamina 
3
Manual dexterity 
4
Gross body coordination 
4
Foreign language 
5
Dynamic flexibility 
5
Public safety and security 
6
Personnel and human resources 
6
Static strength 
7
Developing objectives and strategies 
7
Armhand steadiness 
8
Ability to maintain balance 
8
Controlling machines and processes 
9
Developing and building teams 
9
Finger dexterity 
10
Coaching and developing others 
10
Production and processing 
Athletes and Sports Competitors use these skills the least  Agricultural Graders use these skills the least 

1
Equipment maintenance 
1
Information ordering 
2
Ability to organize groups in different ways 
2
Far vision 
3
Quality control analysis 
3
Pattern recognition 
4
Troubleshooting 
4
Near vision 
5
Number facility 
5
Making decisions and solving problems 
6
Operation and control 
6
Reading comprehension 
7
Mathematics 
7
Active learning 
8
Ability to determine where a sound comes from 
8
Complex problem solving 
9
Mathematical reasoning 
9
Processing information 
10
Science 
10
Time management 
The opposite job of a taxi driver and chauffeur is a physicist.
Taxi Drivers and Chauffeurs use these skills the most  Physicists use these skills the most 

1
Peripheral vision 
1
Physics 
2
Ability to determine where a sound comes from 
2
Mathematical reasoning 
3
Ability to react quickly in response to signals 
3
Number facility 
4
Ability to to time movements in anticipation of moving objects 
4
Ability to organize groups in different ways 
5
Night vision 
5
Information ordering 
6
Spatial orientation 
6
Mathematics 
7
Transportation 
7
Oral comprehension 
8
Glare sensitivity 
8
Mathematics 
9
Reaction time 
9
Originality 
10
Multitasking 
10
Speech clarity 
Taxi Drivers and Chauffeurs use these skills the least  Physicists use these skills the least 

1
Computers and electronics 
1
Performing general physical activities 
2
Interacting with computers 
2
Handling and moving objects 
3
Education and training 
3
Operation and control 
4
Ability to organize groups in different ways 
4
Customer and personal service 
5
Management of personnel resources 
5
Production and processing 
6
Coordinating the work and activities of others 
6
Assisting and caring for others 
7
Repairing 
7
Personnel and human resources 
8
Information ordering 
8
Drafting, laying out and specifying technical devices, parts and equipment 
9
Perceptual speed 
9
Performing for or working directly with the public 
10
Near vision 
10
Controlling machines and processes 
CAREERS: BBC: Broadcast Journalist (Afaan Oromo) August 15, 2017
Posted by OromianEconomist in Afaan Oromoo, BBC Afaan Oromoo.Tags: #BBCAfaanOromoo, Afaan Oromo, Africa, BBC Afaan Oromoo, BBC Afan Oromo, BBC World Service, Oromia, Oromo
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Broadcast Journalist (Afaan Oromo)
Job Introduction
BBC World Service is an international multimedia broadcaster, part of BBC News, delivering a wide range of language and regional services and working increasingly with other parts of BBC News to serve global audiences. It uses multiple platforms to reach its weekly audience of 320 million globally, including TV, digital platforms including social media, AM, FM, shortwave, digital satellite and cable channels.
As part of an historic and exciting expansion the BBC World Service is expanding its language services serving audiences in 12 new languages. All Language Services are multiplatform, with a multimedia website with a focus on digital video, text, interactivity for both desktop and mobile platforms, and a daily TV news programmes for each service.
Role Responsibility
1. To research, interview original sources and write reports, analysis and features for the BBC Afaan Oromo website in a range of formats.
2. To help produce and/or present the BBC’s live radio programme.
3. To respond to breaking stories whilst on air and to resolve technical difficulties.
4. To create content to drive the BBC Facebook page and other social media platforms.
5. To be able to conduct interviews in audio and video on request, on phone or facetoface, with authority and indepth knowledge about the region.
6. To ensure that all output material for which the post holder is responsible meets the standards required by the BBC.
7. To use journalistic skills and experience to suggest new angles on existing stories, means of moving the story on, and to put forward stories not yet covered.
8. To use editorial skills as appropriate to edit, write and adapt the material for the outputs on Facebook and other relevant platforms as required whilst maintaining professional journalistic standards of accuracy, impartiality and fair dealing and adhering to the BBC’s Producers guidelines.
9. To create material for all multimedia outputs, including text stories, audio bulletins and – with appropriate training – video reports for both BBC Afaan Oromo online and Facebook or other social media platforms
10. To ensure that BBC Editorial principles of balance and impartiality and all relevant legal, contractual and copyright requirements are met, referring upwards in cases of difficulty or doubt.
11. To build and maintain links with other areas of the BBC, including BBC World Service Online and BBC News, to enable the efficient production of content.
The Ideal Candidate
1. A full command and up to date knowledge of written and spoken Afaan Oromo.
2. A good knowledge of English, including complete comprehension of written and spoken English and the ability to communicate effectively.
3. Wide and up to date familiarity with the target area and an indepth understanding of its history, politics, social issues and culture as well as the changing needs of the audience.
4. Recent and relevant experience as a journalist and/or reporter would be preferable but not essential.
5. Ability to write, adapt and translate with accuracy, clarity and style appropriate to differing audiences and forms of social media.
6. A good broadcasting voice and the ability to acquire an appropriate presentation.
7. Able to demonstrate a good range of contacts for interview purposes from all walks of life.
8. Good keyboard/computer skills and the ability to acquire technical skills and to operate technical equipment. Practical experience and extensive knowledge of the Internet and an understanding of the potential of new technology is essential.
9. A thorough knowledge and understanding of news and current affairs in the target area as well as a good knowledge of and interest in, international and regional affairs.
10. A thorough
Package Description
Grade: Local Terms and Conditions Apply.
About the Company
We don’t focus simply on what we do – we also care how we do it. Our values and the way we behave are very important to us. Please make sure you’ve read about our values and behaviours in the document attached below. You’ll be asked questions relating to them as part of your application for this role.
The BBC is committed to building a culturally diverse workforce and therefore strongly encourages applications from underrepresented groups. We are committed to equality of opportunity and welcome applications from individuals, regardless of their background.
Time Series Data and Machine Learning August 15, 2017
Posted by OromianEconomist in 10 best Youtube videos, 25 killer Websites that make you cleverer, Data Science, Econometrics, Economics, Uncategorized.Tags: Anomaly Detection, Deep Learning, Econometrics, economics, Machine Learning, Statistics, Time Series Databases
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Anomaly Detection of Time Series Data Using Machine Learning & Deep Learning
Introduction to Time Series Data
By XenonStack, June 23, 2017
Time Series is defined as a set of observations taken at a particular period of time. For example, having a set of login details at regular interval of time of each user can be categorized as a time series. On the other hand, when the data is collected at once or irregularly, it is not taken as a time series data.
Time series data can be classified into two types –

Stock Series – It is a measure of attributes at a particular point in time and taken as a stock takes.

Flow Series – It is a measure of activity at a specific interval of time. It contains effects related to the calendar.
Time series is a sequence that is taken successively at the equally pace of time. It appears naturally in many application areas such as economics, science, environment, medicine, etc. There are many practical real life problems where data might be correlated with each other and are observed sequentially at the equal period of time. This is because, if the repeatedly observe the data at a regular interval of time, it is obvious that data would be correlated with each other.
With the use of time series, it becomes possible to imagine what will happen in the future as future event depends upon the current situation. It is useful to divide the time series into historical and validation period. The model is built to make predictions on the basis of historical data and then this model is applied to the validation set of observations. With this process, the idea is developed how the model will perform in forecasting.
Time Series is also known as the stochastic process as it represents the vector of stochastic variables observed at regular interval of time.
Components of Time Series Data
In order to analyze the time series data, there is a need to understand the underlying pattern of data ordered at a particular time. This pattern is composed of different components which collectively yield the set of observations of time series.
The Components of time series data are given below –

Trend

Cyclical

Seasonal

Irregular
Trend – It is a long pattern present in the time series. It produces irregular effects and can be positive, negative, linear or nonlinear. It represents the variations of low frequency and the high and medium frequency of data is filtered out from the time series.
If the time series does not contain any increasing or decreasing pattern, then time series is taken as stationary in the mean.
There are two types of the trend –

Deterministic – In this case, the effects of the shocks present in the time series are eliminated i.e. revert to the trend in long run.

Stochastic – It is the process in which the effects of shocks are never eliminated as they have permanently changed the level of the time series.
The stochastic process having a stationarity around the deterministic process is known as trend stationary process.
Cyclic – The pattern exhibit up and down movements around a specified trend is known as cyclic pattern. It is a kind of oscillations present in the time series. The duration of cyclic pattern depends upon the industries and business problems to be analysed. This is because the oscillations are dependable upon the business cycle.
They are larger variations that are repeated in a systematic way over time. The period of time is not fixed and usually composed of at least 2 months in duration. The cyclic pattern is represented by a wellshaped curve and shows contraction and expansion of data.
Seasonal – It is a pattern that reflects regular fluctuations. These shortterm movements occur due to the seasonal factors and custom factors of people. In this case, the data faces regular and predictable changes that occurred at regular intervals of calendar. It always consist of fixed and known period.
The main sources of seasonality are given below –

Climate

Institutions

Social habits and practices

Calendar
How is the seasonal component estimated?
If the deterministic analysis is performed, then the seasonality will remain same for similar interval of time. Therefore, it can easily be modelled by dummy variables. On the other hand, this concept is not fulfilled by stochastic analysis. So, dummy variables are not appropriate because the seasonal component changes throughout the time series.
Different models to create a seasonal component in time series are given below –

Additive Model – It is the model in which the seasonal component is added with the trend component.

Multiplicative Model – In this model seasonal component is multiplied with the intercept if trend component is not present in the time series. But, if time series have trend component, sum of intercept and trend is multiplied with the seasonal component.
Irregular – It is an unpredictable component of time series. This component cannot be explained by any other component of time series because these variational fluctuations are known as random component. When the trend cycle and seasonal component is removed, it becomes residual time series. These are short term fluctuations that are not systematic in nature and have unclear patterns.
Difference between Time Series Data and CrossSection Data
Time Series Data is composed of collection of data of one specific variable at particular interval of time. On the other hand, CrossSection Data is consist of collection of data on multiple variables from different sources at a particular interval of time.
Collection of company’s stock market data at regular interval of year is an example of time series data. But when the collection of company’s sales revenue, sales volume is collected for the past 3 months then it is taken as an example of crosssection data.
Time series data is mainly used for obtaining results over an extended period of time but, crosssection data focuses on the information received from surveys at a particular time.
What is Time Series Analysis?
Performing analysis of time series data is known as Time Series Analysis. Analysis is performed in order to understand the structure and functions produced by the time series. By understanding the mechanism of time series data a mathematical model could easily be developed so that further predictions, monitoring and control can be performed.
Two approaches are used for analyzing time series data are –

In the time domain

In the frequency domain
Time series analysis is mainly used for –

Decomposing the time series

Identifying and modeling the timebased dependencies

Forecasting

Identifying and model the system variation
Need of Time Series Analysis
In order to model successfully, the time series is important in machine learning and deep learning. Time series analysis is used to understand the internal structure and functions that are used for producing the observations. Time Series analysis is used for –

Descriptive – In this case, patterns are identified in correlated data. In other words, the variations in trends and seasonality in the time series are identified.

Explanation – In this understanding and modeling of data is performed.

Forecasting – Here, the prediction from previous observations is performed for short term trends.

Invention Analysis – In this case, effect performed by any event in time series data is analyzed.

Quality Control – When the specific size deviates it provides an alert.
Applications of Time Series Analysis
Time Series Database and its types
Time series database is a software which is used for handling the time series data. Highly complex data such higher transactional data is not feasible for the relational database management system. Many relational systems does not work properly for time series data. Therefore, time series databases are optimised for the time series data. Various time series databases are given below –

CrateDB

Graphite

InfluxDB

Informix TimeSeries

Kx kdb+

RiakTS

RRDtool

OpenTSDB
What is Anomaly?
Anomaly is defined as something that deviates from the normal behaviour or what is expected. For more clarity let’s take an example of bank transaction. Suppose you have a saving bank account and you mostly withdraw Rs 10,000 but, one day Rs 6,00,000 amount is withdrawn from your account. This is unusual activity for bank as mostly, Rs 10,000 is deducted from the account. This transaction is an anomaly for bank employees.
The anomaly is a kind of contradictory observation in the data. It gives the proof that certain model or assumption does not fit into the problem statement.
Different Types of Anomalies
Different types of anomalies are given below –

Point Anomalies – If the specific value within the dataset is anomalous with respect to the complete data then it is known as Point Anomalies. The above mentioned example of bank transaction is an example of point anomalies.

Contextual Anomalies – If the occurrence of data is anomalous for specific circumstances, then it is known as Contextual Anomalies. For example, the anomaly occurs at a specific interval of period.

Collective Anomalies – If the collection of occurrence of data is anomalous with respect to the rest of dataset then it is known as Collective Anomalies. For example, breaking the trend observed in ECG.
Models of Time Series Data
ARIMA Model – ARIMA stands for Autoregressive Integrated Moving Average. Auto Regressive (AR) refers as lags of the differenced series, Moving Average (MA) is lags of errors and I represents the number of difference used to make the time series stationary.
Assumptions followed while implementing ARIMA Model are as under –

Time series data should posses stationary property: this means that the data should be independent of time. Time series consist of cyclic behaviour and white noise is also taken as a stationary.

ARIMA model is used for a single variable. The process is meant for regression with the past values.
In order to remove nonstationarity from the time series data the steps given below are followed –

Find the difference between the consecutive observations.

For stabilizing the variance log or square root of the time series data is computed.

If the time series consists of the trend, then the residual from the fitted curve is modulated.
ARIMA model is used for predicting the future values by taking the linear combination of past values and past errors. The ARIMA models are used for modeling time series having random walk processes and characteristics such as trend, seasonal and nonseasonal time series.
HoltWinters – It is a model which is used for forecasting the short term period. It is usually applied to achieve exponential smoothing using additive and multiplicative models along with increasing or decreasing trends and seasonality. Smoothing is measured by beta and gamma parameters in the holt’s method.

When the beta parameter is set to FALSE, the function performs exponential smoothing.

The gamma parameter is used for the seasonal component. If the gamma parameter is set to FALSE, a nonseasonal model is fitted.
How to find Anomaly in Time Series Data
AnomalyDetection R package –
It is a robust open source package used to find anomalies in the presence of seasonality and trend. This package is build on Generalised ETest and uses Seasonal Hybrid ESD (SHESD) algorithm. SHESD is used to find both local and global anomalies. This package is also used to detect anomalies present in a vector of numerical variables. Is also provides better visualization such that the user can specify the direction of anomalies.
Principal Component Analysis –
It is a statistical technique used to reduce higher dimensional data into lower dimensional data without any loss of information. Therefore, this technique can be used for developing the model of anomaly detection. This technique is useful at that time of situation when sufficient samples are difficult to obtain. So, PCA is used in which model is trained using available features to obtain a normal class and then distance metrics is used to determine the anomalies.
Chisq Square distribution –
It is a kind of statistical distribution that constitutes 0 as minimum value and no bound for the maximum value. Chisq square test is implemented for detecting outliers from univariate variables. It detects both lowest and highest values due to the presence of outliers on both side of the data.
What are Breakouts in Time Series Data?
Breakout are significant changes observed in the time series data. It consist of two characteristics that are given below –

Mean shift – It is defined as a sudden change in time series. For example the usage of CPU is increased from 35% to 70%. This is taken as a mean shift. It is added when the time series move from one steady state to another state.

Ramp Up – It is defined as a sudden increase in the value of the metric from one steady state to another. It is a slow process as compared with the mean shift. It is a slow transition process from one stable state to another.
In Time series often more than one breakouts are observed.
How to detect Breakouts in Time Series Data?
In order to detect breakouts in time series Twitter has introduced a package known as BreakoutDetection package. It is an open source package for detecting breakouts at a fast speed. This package uses EDivisive with Medians (EDM) algorithm to detect the divergence within the mean. It can also be used to detect the change in distribution within the time series.
Need of Machine Learning and Deep Learning in Time Series Data
Machine learning techniques are more effective as compared with the statistical techniques. This is because machine learning have two important features such as feature engineering and prediction. The feature engineering aspect is used to address the trend and seasonality issues of time series data. The issues of fitting the model to time series data can also be resolved by it.
Deep Learning is used to combine the feature extraction of time series with the nonlinear autoregressive model for higher level prediction. It is used to extract the useful information from the features automatically without using any human effort or complex statistical techniques.
Anomaly Detection using Machine Learning
There are two most effective techniques of machine learning such as supervised and unsupervised learning.
Firstly, supervised learning is performed for training data points so that they can be classified into anomalous and nonanomalous data points. But, for supervised learning, there should be labeled anomalous data points.
Another approach for detecting anomaly is unsupervised learning. One can apply unsupervised learning to train CART so that prediction of next data points in the series could be made. To implement this, confidence interval or prediction error is made. Therefore, to detect anomalous data points Generalised ESDTest is implemented to check which data points are present within or outside the confidence interval
The most common supervised learning algorithms are supervised neural networks, support vector machine learning, knearest neighbors, Bayesian networks and Decision trees.
In the case of knearest neighbors, the approximate distance between the data points is calculated and then the assignment of unlabeled data points is made according to the class of knearest neighbor.
On the other hand, Bayesian networks can encode the probabilistic relationships between the variables. This algorithm is mostly used with the combination of statistical techniques.
The most common unsupervised algorithms are selforganizing maps (SOM), Kmeans, Cmeans, expectationmaximization metaalgorithm (EM), adaptive resonance theory (ART), and oneclass support vector machine.
Anomaly Detection using Deep Learning
Recurrent neural network is one of the deep learning algorithm for detecting anomalous data points within the time series. It consist of input layer, hidden layer and output layer. The nodes within hidden layer are responsible for handling internal state and memory. They both will be updated as the new input is fed into the network. The internal state of RNN is used to process the sequence of inputs. The important feature of memory is that it can automatically learns the timedependent features.
The process followed by RNN is described below –
First the series of data is fed into the RNN model. After that, model will train the series of data to compute the normal behaviour. After computing, whenever the new input is fed into the trained network, it will be able to classify the input as normal and expected, or anomalous.
Training of normal data is performed because the quantity of abnormal data is less as compared with the normal data and provides an alert whenever any abnormal activity is observed in the future.
Time Series Data Visualization
Data Visualization is an important and quickest way for picturizing the time series data and forecasting. The different types of graphs are given below:

Line Plots.

Histograms and Density Plots.

Box and Whisker Plots.

Heat Maps.

Lag Plots or Scatter Plots.

Autocorrelation Plots.
The above techniques are used for plotting univariate time series data but they can also be used for multivariate time series when more than one observation is dependent upon time.
They are used for the representation of time series data to identify trends, cycles, and seasonality from time series and observe how they can influence the choice of model.
Summary
Time Series is defined as sequence of data points. The components of time series are responsible for the understanding of patterns of data. In time series, anomalous data points can also be there.
Therefore, there is a need to detect them. Various statistical techniques are mentioned in blog that are used but machine learning and deep learning are essential.
In machine learning, supervised learning and unsupervised learning is used for detecting anomalous data. On the other hand, in deep learning recurrent neural network is used.
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