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Time Series Analysis Part I

 

A time series is a sequence of numerical data in which each item is associated with a particular instant in time. Many sets of data appear as time series: a monthly sequence of the quantity of goods shipped from a factory, a weekly series of the number of road accidents, daily rainfall amounts, hourly observations made on the yield of a chemical process, and so on. Examples of time series abound in such fields as economics, business, engineering, the natural sciences (especially geophysics and meteorology), and the social sciences.

  • Univariate time series analysis- When we have a single sequence of data observed over time then it is called univariate time series analysis.
  • Multivariate time series analysis – When we have several sets of data for the same sequence of time periods to observe then it is called multivariate time series analysis.

The data used in time series analysis is a random variable (Yt) where t is denoted as time and such a collection of random variables ordered in time is called random or stochastic process.

Stationary: A time series is said to be stationary when all the moments of its probability distribution i.e. mean, variance , covariance etc. are invariant over time. It becomes quite easy forecast data in this kind of situation as the hidden patterns are recognizable which make predictions easy.

Non-stationary: A non-stationary time series will have a time varying mean or time varying variance or both, which makes it impossible to generalize the time series over other time periods.

Non stationary processes can further be explained with the help of a term called Random walk models. This term or theory usually is used in stock market which assumes that stock prices are independent of each other over time. Now there are two types of random walks:
Random walk with drift : When the observation that is to be predicted at a time ‘t’ is equal to last period’s value plus a constant or a drift (α) and the residual term (ε). It can be written as
Yt= α + Yt-1 + εt
The equation shows that Yt drifts upwards or downwards depending upon α being positive or negative and the mean and the variance also increases over time.
Random walk without drift: The random walk without a drift model observes that the values to be predicted at time ‘t’ is equal to last past period’s value plus a random shock.
Yt= Yt-1 + εt
Consider that the effect in one unit shock then the process started at some time 0 with a value of Y0
When t=1
Y1= Y0 + ε1
When t=2
Y2= Y1+ ε2= Y0 + ε1+ ε2
In general,
Yt= Y0+∑ εt
In this case as t increases the variance increases indefinitely whereas the mean value of Y is equal to its initial or starting value. Therefore the random walk model without drift is a non-stationary process.

So, with that we come to the end of the discussion on the Time Series. Hopefully it helped you understand time Series, for more information you can also watch the video tutorial attached down this blog. DexLab Analytics offers machine learning courses in delhi. To keep on learning more, follow DexLab Analytics blog.


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Top 5 Industry Use Cases of Predictive Analytics

Top 5 Industry Use Cases of Predictive Analytics

Predictive analytics is an effective in-hand tool crafted for data scientists. Thanks to its quick computing and on-point forecasting abilities! Not only data scientists, but also insurance claim analysts, retail managers and healthcare professionals enjoy the perks of predictive analytics modeling – want to know how?

Below, we’ve enumerated a few real-life use cases, existing across industries, threaded with the power of data science and predictive analytics. Ask us, if you have any queries for your next data science project! Our data science courses in Delhi might be of some help.

Customer Retention

Losing customers is awful. For businesses. They have to gain new customers to make up for the loss in revenue. But, it can cost more, winning new customers is usually hailed more costly than retaining older ones.

Predictive analytics is the answer. It can prevent reduction in the customer base. How? By foretelling you the signs of customer dissatisfaction and identifying the customers that are most likely to leave. In this way, you would know how to keep your customers satisfied and content, and control revenue slip offs.

Customer Lifetime Value

Marketing a product is the crux of the matter. Identifying customers willing to spend a large part of their money, consistently for a long period of time is difficult to find. But once cracked, it helps companies optimize their marketing efforts and enhance their customer lifetime value.

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Quality Control

Quality Control is significant. Over time, shoddy quality control measures will affect customer satisfaction ratio, purchasing behavior, thus impacting revenue generation and market share.

Further, low quality control results in more customer support expenses, repairs and warranty challenges and less systematic manufacturing. Predictive analytics help provide insights on potential quality issues, before they turn into crucial company growth hindrances.

Risk Modeling

Risk can originate from a plethora of source, and it can be any form. Predictive analytics can address critical aspects of risk – it collects a huge number of data points from many organizations and sort through them to determine the potential areas of concern.

What’s more, the trends in the data hint towards unfavorable circumstances that might impact businesses and bottom line in an adverse way. A concoction of these analytics and a sound risk management approach is what companies truly need to quantify the risk challenges and devise a perfect course of action that’s indeed the need of the hour.

Sentiment Analysis

It’s impossible to be everywhere, especially when being online. Similarly, it’s very difficult to oversee everything that’s said about your company.

Nevertheless, if you amalgamate web search and a few crawling tools with customer feedback and posts, you’d be able to develop analytics that’d present you an overview of the organization’s reputation along with its key market demographics and more. Recommendation system helps!

All hail Predictive Analytics! Now, maneuver beyond fuss-free reactive operations and let predictive analytics help you plan for a successful future, evaluating newer areas of business scopes and capabilities.

Interested in data science certification? Look up to the experts at DexLab Analytics.

The blog has been sourced fromxmpro.com/10-predictive-analytics-use-cases-by-industry

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Predictive Analytics: The Key to Enhance the Process of Debt Collection

Predictive Analytics: The Key to Enhance the Process of Debt Collection

A wide array of industries has already engaged in some kind of predictive analytics – numerical analysis of debt collection is relatively a recent addition. Financial analysts are now found harnessing the power of predictive analytics to cull better results out for their clients, and measure the effectiveness of their strategies and collections.

Let’s see how predictive analytics is used in debt collection process:

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Understanding Client Scoring (Risk Assessment)

Since the late 1980’s, FICO score is regarded as the golden standard for determining creditworthiness and loan application. But, however, machine learning, particularly predictive analytics can replace it, and develop an encompassing portrait of a client, taking into effect more than his mere credit history and present debts. It can also include his social media feeds and spending trajectory.

Evaluating Payment Patterns

The survival models evaluate each client’s probability of becoming a potential loss. If the account shows a continuous downward trend, then it should be regarded soon as a potential risk. Predictive analytics can help identify spending patterns, indicating the struggles of each client. A system can be developed which self-triggers whenever any unwanted pattern transpires. It could ask the client if they need any help or if they are going through a financial distress, so that it can help before the situation turns beyond repairs.

For R predictive modeling training courses, visit DexLab Analytics.

Cash Flow Predictions

Businesses are keen to know about future cash flows – what they can expect! Financial institutions are no different. Predictive analytics helps in making more appropriate predictions, especially when it comes to receivables.

Debt collector’s business models are subject to the ability to forecast the success of collection operations, and ascertaining results at the end of each month, before the billing cycle initiates. As a result, the workforce of the company is able to shift their focus from the potential payers to those who would not be able to meet their obligations. This shift in focus helps!

Better Client Relationship

Predictive analytics weave wonders; not only it has the ability to point which clients are the highest risks for your company, but also predict the best time to contact them to reap maximum results. What you need to do is just visit the logs of past conversations.

Challenges

Last, but not the least, all big data models face a common challenge – data cleaning. As it’s a process of wastage in and out, before starting with prediction, company should deal with this problem at first to construct a pipeline, for feeding in the data, clean it and use it for neural network training.

In a concluding statement, predictive analytics is the best bet for debt and revenue collection – it boosts conversion rates at the right time with the right people. If you want to study more about predictive analytics, and its varying uses in different segments of industry, enroll in R Predictive Modelling Certification training at DexLab Analytics. They provide superior knowledge-intensive training to interested individuals with added benefit of placement assistance. For more, visit their website.

 

The blog has been sourced fromdataconomy.com/2018/09/improving-debt-collection-with-predictive-models

 

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Predictive Analytics: What It is and Why It’s Important for Businesses

Predictive Analytics: What It is and Why It’s Important for Businesses

Did you know that 2.5 quintillion bytes of data are generated on a daily basis? Big data is a valuable asset for companies provided that this data can be utilized to improve their performance. Companies employ predictive analytics to uncover hidden patterns in data and develop quick and efficient strategies that will steer their businesses forward.

IMB Watson is a popular predictive analytics processor that employs natural language processing technology to analyze human speech. IBM Watson can analyze a vast amount of data, often in a fraction of a second, to answer human-framed questions.

What is predictive analytics?

Predictive analytics use a combination of statistical modeling and machine learning techniques to determine the likelihood of future events based on historical data, which can come from structured, unstructured and semi-structured sources. A good example of the use of predictive analytics is the preparation of a credit report of a customer by a financial institution.

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Credit Score:

Financial lenders use predictive analytics to scrutinize relevant data of an individual who has applied for a loan, including data pertaining to the individual’s current assets and debts, his/her employment and history of paying off loans. All this data is analyzed and boiled down to a single value known as credit score. This value represents the lending risk and helps the lender determine a customer’s creditworthiness. The higher the credit score, the more confident is the lender that the customer will fulfill his/her credit obligation.

predictive_analytics_and_cross-selling-01_1

Predictive analytics help lenders make quick and efficient decisions, such as accepting or rejecting a customer and increasing or decreasing their loan value. Credit risk modeling training has become extremely important across many sectors, including banking, insurance and retail.

Importance of predictive analytics:

Thanks to the plethora or new age analytics tools and software, predictive analytics make it easier for organizations to plan the future and gain competitive advantage.

Below are some ways in which predictive analytics are used:

  • To predict the probability of certain diseases affecting a specific group of people so that the necessary preventive healthcare measures can be taken.
  • To predict the probability of certain machine parts failing so that preventive maintenance can be administered.
  • To predict the probability of an interruption in a business’s supply chain.
  • To predict customer behavior.
  • To predict safety risks on railroads.
  • To predict traffic flows and the infrastructure requirements of a city.

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How businesses use predictive analytics:

It is imperative for every company to include predictive analytics in their technology portfolio. The major vendors of predictive analytics include SAP, IBM, Oracle, SAS, Information Builders, etc. Their on-premise and cloud-based versions give companies a lot of options to choose their predictive analytics tools from.

On-premise predictive analytics systems are used by companies requiring high level of analytical power and predictive intelligence. These include companies in the drug and pharmaceutical sector; companies working on life science fields like genomics; and research institutes and universities.

Cloud-based versions provide predictive analytics solutions to companies on a per usage or subscription basis. These are highly beneficial for small and medium sized companies where predictive analytics aren’t the core component, but they are still critical for their success and need to be fitted in a stipulated IT budget. Companies can use the ‘’try and buy’’ facility provided by cloud-vendors to test if a particular software is working for them before finalizing a contract.

Companies that lack prior experience in predictive analytics can opt for SaaS (Software as a Service), which are cloud-based solutions with expertise in a specific sector, for example healthcare.

Role of Business Leaders:

Business leaders must be skilled in using the insights provided by predictive analytics to develop strategies that drive their businesses forward. This includes two things; firstly coming up with well-construed questions and secondly identifying the right kind of data to analyze. These will determine whether predictive analytics is working for a company or not.

Companies in all industry verticals are employing predictive analytics to formulate future strategies. As mentioned in a report- ‘’the global market for predictive analytics is projected to grow to $3.6 billion USD by 2020.”

To more about predictive analytics follow Dexlab Analytics– a premier analytics training institute in Gurgaon. Do take a look at their credit risk modeling courses.

 

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Why B2B Marketers Should Use Predictive Analytics in 2018?

The Risks are indispensable. In any business.

But what if you can nullify the risks?

Or find a suitable solution to limit them?

Why B2B Marketers Should Use Predictive Analytics in 2018?

That’s probably where Predictive Analytics ring the bell. Right from logistics and inventory to marketing initiatives and sales to applications in hiring and HR, predictive analytics is the ultimate tool that impacts business decisions across every domain of a B2B enterprise.

Continue reading “Why B2B Marketers Should Use Predictive Analytics in 2018?”

Bringing Back Science into “Data Science”

Bringing Back Science into “Data Science”

Far from the conventional science disciplines, like physics or mathematics, Data Science is a budding discipline: which means there are no proper definition to explain what data science is and what role it does play.

Nevertheless, the internet is full of working definitions of data science. As per Wikipedia, Data Science is

(an) interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.

To that note, a very important aspect is left behind in this explanation: Data Science is a science first, which means a proper scientific method should be devised to tackle different data science practices. By scientific method, we mean a healthy process of asking questions, collecting information, framing hypothesis and analyzing the results to draw conclusions thereafter.

Go below, the process breakup is as follows..

Ask questions

Start by asking what is the business problem? How to leverage maximum gains? What ways to implement to increase return on investment? The finance industry takes help from data science for myriad reasons. One of the most striking reasons is to enhance the return on investment out of marketing campaigns.

What Sets Apart Data Science from Big Data and Data Analytics – @Dexlabanalytics.

Collect data

A predictive modeling analyst has access to vast data resources, which eventually makes the entire research and gathering data process much less complex. However, it is only in theory, because rarely data is stored in the desired format an analyst wants, making his job easier.

Data Science – then and now! – @Dexlabanalytics.

Devise a hypothesis

After getting to the heart and soul of the problem, we start to develop hypotheses. For example, you believe your firm’s profit is leveraged by an optimistic customer reaction towards your product quality and positive advertising capabilities of your firm. Through this example, we explained a nomological network, where you are in a position to infer casualties and correlations. While dealing in Data Science, assessing customer perception is very crucial, and so is the analysis of financial datasets.

Data Science: Is It the Right Answer? – @Dexlabanalytics.

Testing and experiments

Formulating a hypothesis is not enough; a predictive modeler relies on statistical modeling techniques to forecast the future in a probabilistic manner. Keep a note, this doesn’t result in indicating “X will occur”, instead it refers “Given Y, the probability of X occurring is 75%.”

Any proper experiment includes control groups and test, meaning a modeler when preparing a predictive model should divide the dataset so as to ensure availability of few data for testing predictive equation.

Now, if we talk about marketing – consider logistic regression. It offers a probability whether a binary event of interest will take place or not.

Enroll in an R Predictive Modelling Certification program to go through the mechanics of this problem. Reach us at DexLab Analytics.

Tracing Success in the New Age of Data Science – @Dexlabanalytics.

Evaluate results and infer conclusions

Now is the time to make a decision: do you prefer the quantitative approach? As social media is totally unstructured, the qualitative approach needs to be implemented using Natural Language Processing, which can be a tad difficult. Now, how about making a longitudinal analysis, while transforming data into time series? Do all these questions rake your mind? Yes? Then you are on the right track.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

Reporting of results

This is the final battle scene for all predictive modelers. It calls for all the documents, based on which a modeler made his decision during the development process. All the assumptions taken have to be identified and highlighted beside the results.

And with it comes the end of our Science in Data Science process!

For more interesting updates and blogs, follow us at DexLab Analytics. Opt for our impressive Data Science Courses in gurgaon and lead the road of success!

 

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Keep Pace with Automation: Emerging Data Science Jobs in India

Indian IT market is not yet doomed. In fact, if you look at the larger picture, you will find India is expected to face a shortage of 200000 data scientists by 2020. Where traditional IT jobs are going through a rough patch, new age jobs are surfacing up, according to market reports. Big Data, Artificial Intelligence, the Internet of Things, Cloud Computing, and Cybersecurity are new digital domains that are replacing the old school jobs, like data entry and server maintenance, which are expected to reduce more over the next five years.
The next decade is going to witness most vacancies in these job posts:

However, just because there is a wide array of openings for a web services consultant doesn’t make it the most lucrative job position. Big Data architect job openings are much less in number, but offer handsome pays, according to reports.

A median salary of a web services consultant is Rs 9.27 lakh ($14,461) annually

A median salary of a big data architect is Rs 20.67 lakh ($32,234) annually

Now, tell me, which is better?

As technologies evolve so drastically, it becomes an absolute imperative for the techies to update their skills through short learning programs and crash courses. Data analyst courses will help them to sync in with the latest technological developments, which happens every day, something or the other. Moreover, it’s like a constant process, where they have to learn something every year to succeed in this rat race of technological superiority. Every employee needs to make some time, as well as the companies. The companies also need to facilitate these newer technologies in their systems to keep moving ahead of their tailing rivals.

Re-skill or perish – is the new slogan going around. The urgency to re-skill is creating a spur among employees with mid-level experience. If you check the surveys, you will find around 57% of the 7000 IT professionals looking forward to enroll for a short time learning course have at least 4 to 10 years of work experience. Meanwhile, a mere 11% of those who are under 4 years of experience are looking out for such online courses. It happens because, primary-stage employees are mostly fresh graduates, who receives in-house training from their respective companies, hence they don’t feel the urge to scrounge through myriad learning resources, unlike their experienced counterparts.

 

 

Today, all big companies across sectors are focusing their attention on data science and analytics, triggering major reinventions in the job profile of a data analyst. Owing to technology updates, “The role of a data analyst is itself undergoing a sea change, primarily because better technology is available now to aid in decision-making,” said Sumit Mitra, head of group human resources and corporate services at GILAC. To draw a closure, data science is the new kid in the block, and IT professionals are imbibing related skills to shine bright in this domain. Contact DexLab Analytics for data analyst course in Delhi. They offer high-in demand data analyst certification courses at the most affordable prices.

 

The ABC of Summary Statistics and T Tests in SAS

The ABC of Summary Statistics and T Tests in SAS

Getting introduced to statistics for SAS training? Then, you must know how to create summary statistics (such as sample size, mean, and standard deviation) to test hypotheses and to figure confidence intervals. In this blog, we will show you how to furnish summary statistics (instead of raw data) to PROC TTEST in SAS, how to develop a data set that includes summary statistics and how to run PROC TTEST to calculate a two-sample or one-sample t test for the mean.

So, let’s start!

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Running a two-sample t test for difference of means from summarized statistics

Instead of going the clichéd way, we will start with establishing a comparison between the mean heights of 19 students, based on gender – the data is held in the Sashelp class data set.

Observe the below SAS statements that sorts the data by the grouping variable, calling PROC MEANS and printing a subset of the statistics:

proc sort data=sashelp.class out=class; 
   by sex;                                /* sort by group variable */
run;
proc means data=class noprint;           /* compute summary statistics by group */
   by sex;                               /* group variable */
   var height;                           /* analysis variable */
   output out=SummaryStats;              /* write statistics to data set */
run;
proc print data=SummaryStats label noobs; 
   where _STAT_ in ("N", "MEAN", "STD");
   var Sex _STAT_ Height;
run;

summarystats1

The table reflects the structure of the Summary Stats set for two sample tests. The two samples used here are differentiated on the levels of the Sex Variable (‘F’ for females and ‘M’ for males). The _STAT_ column shows the name of the statistic implemented here. The Height column depicts the value of the statistics for individual group.

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The problem: The heights of sixth-grade students are normally distributed. Random samples of n1=9 females and n2=10 males are selected. The mean height of the female sample is m1=60.5889 with a standard deviation of s1=5.0183. The mean height of the male sample is m2=63.9100 with a standard deviation of s2=4.9379. Is there evidence that the mean height of sixth-grade students depends on gender?

Here, you have to do nothing special to get the PROC TTEST – whenever the procedure gets the sight of the respective variable _STAT_ and any unique values, the procedure understands that the data set comprises summarized statistics. The following representation compares the mean heights of males and females:

proc ttest data=SummaryStats order=data
           alpha=0.05 test=diff sides=2; /* two-sided test of diff between group means */
   class sex;
   var height;
run;

summarystats1

Check the confidence intervals for the standard deviations and also that the output includes 95% confidence intervals for group means.

In the second table, the ‘Pooled’ row radiates out the impression that both the variances of two groups are more or less equal, which is somewhat true even. The value of the t statistic is t = -1.45 with a two-sided p-value of 0.1645.

The syntax for the PROC TTEST statement allows you to change the type of hypothesis test and the significance level. To support this, you can now run a one-sided test for the alternative hypothesis μ1 < μ2 at the 0.10 significance level just by using:

proc ttest ... alpha=0.10 test=diff sides=L;  /* Left-tailed test */

Running a one-sample t test of the mean from summarized statistics

In the above section, you have learnt to create the summary statistics from PROC MEANS. Nevertheless, you can also generate the summary statistic manually, if you lack original data.

The problem: A research study measured the pulse rates of 57 college men and found a mean pulse rate of 70.4211 beats per minute with a standard deviation of 9.9480 beats per minute. Researchers want to know if the mean pulse rate for all college men is different from the current standard of 72 beats per minute.

The following statements jots down the summary statistics for a data set, asks PROC TTEST to perform a one-sample test of the null hypothesis μ = 72 against a two-sided alternative hypothesis:

data SummaryStats;
  infile datalines dsd truncover;
  input _STAT_:$8. X;
datalines;
N, 57
MEAN, 70.4211
STD, 9.9480
;
 
proc ttest data=SummaryStats alpha=0.05 H0=72 sides=2; /* H0: mu=72 vs two-sided alternative */
   var X;
run;

summarystats3 (2)

The outcome is a 95% confidence interval for the mean containing a value 72. The value of the t statistic is t = -1.20, which corresponds to a p-value of 0.2359. Therefore, the data fails in rejecting the null hypothesis at the 0.05 significance level.

For more informative blogs and news about SAS course, drop by our prime SAS predictive modeling training institute DexLab Analytics.

 
This post originally appeared onblogs.sas.com/content/iml/2017/07/03/summary-statistics-t-tests-sas.html
 

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Will GST Boost The Big Data Revolution? The Answer lies Within

It is July 1st, 2017 – the epic day when GST, aka The Goods and Services Tax comes into effect, simplifying the whole tax collection procedure of the nation. From today, there will be a single tax on the supply of goods and services that will replace all other state and central levies. GST is pegged to be one of the most impressive economic tax reforms implemented by PM Narendra Modi to take Bharat to the summit of transparent digitization.

 
Will GST Boost The Big Data Revolution? The Answer lies Within
 

Data is crucial. While it ushers in a greater transparency and simplified tracking through data, it also unleashes the requirement for Data Analytics and ERP solutions. Besides, GST includes several billing software and payment gateway channelization, triggering plenitude of job opportunities in the IT sector. Reports say it is going to be a $1 billion opportunity for IT vendors over the next two years. Quite a lot to think about!

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