Analytics and forecasts

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Predictive Analysis vs Forecasting – Which is best ? (Infographics)

Predictive Analysis vs Forecasting – While it is close to impossible to predict the future, understanding how the market will evolve and consumer trends will shape up is extremely important for brands and companies across all sectors. This is because consumers are an integral part of the success and growth story of any brand. This is because brands and consumers are an integral part of the market ecosystem. So in order to understand this ecosystem, it is important to conduct an in-depth market analysis. This predictive analysis will hep you understand your target audience in a better manner on one hand and enhance and improve brand connect on the other hand. Together, this predictive analysis vs forecasting will help companies to grow in a profitable manner.

This article on Predictive Analysis vs Forecasting is structured as below:-

Predictive Analysis vs Forecasting Infographics

Hadoop, Data Science, Statistics & others

So what exactly is market analysis? Market data analysis is a technique in which brands use all the information available to them about the market and then create a strategy that will in turn help them, make use of the opportunities that exist. By properly understanding the current and future trends of the market, brands can choose the right strategy to get ahead in the market and generate high profits as well. Market analysis is a very aspect of business as it shows the success ratio of any companies and charters the future growth of the company in an effective fashion. In short, a market analysis reports helps a brand to document relevant and important information that can benefit business from the importance of launching a new product/service or how effective an advertising campaign will be in the future.

When conducted in an proper manner, market analysis can help brands to answer the following questions in a comprehensive manner:

  1. Who is our target audience?
  2. What are their needs and basic expectations?
  3. How can I market my products/services in such a manner that they stand out in the market?
  4. Who are my competitors and what are their USP?
  5. How are my advertising campaigns faring in the industry? What is the scope of improvements?
  6. How to reach the next stage of development?
  7. How can we use our resources in a better manner?
  8. Is there a need to change the priorities and objectives of my brand?

A well conducted and researched market analysis can help brands answer all these questions in an important manner. When the answer to these questions are know, it becomes easier for a brand to find a path in which they can implement changes that are good for the overall growth and development of a brand.

After understanding the importance of market analysis, let us look at the three stages that have to be conducted in order to create that analysis. For creating a good analysis, it is important to look into information about the company in an intricate manner. By understanding the past, present and future brands can create a good and comprehensive analysis.

  • Understanding reports of the past: By using the analysis of the past, brands can understand which campaigns were more successful in reaching their target audience. This will also help brands to understand the hurdles and challenges that they encountered while implementing their campaigns and thereby ensure that future campaigns are implemented in a successful and productive manner.
  • Analysing the current market situation: It is very important that companies understand the market and economy in which they are functioning. This is because understanding the market will help companies to not just connect with their target audience but also launch products and services that are in demand by the existing market. This in turn will help companies to maximise their resources, both material and non-material.
  • Predict the future in a successful manner: Market analysis can help companies to forecast the future trends and create plans that can be initiated resulting in maximum advantage, even over the competitors. By creating constant and powerful customer connections and ensuring high return on investments, brands can get better results in the future.

Predictive Analysis vs Forecasting are two methods that can help companies create effective market analysis plans. This is because through these two predictive analysis vs forecasting techniques brands can understand their customers better on one hand and can ensure better products and services on the other hand.

What is predictive analysis and how does predictive analysis work?

Predictive analysis is a technique that leverages statistics in order to predict future outcomes. Predictive Analysis can also be applied to events that have already happened. For instance, predictive analysis can be used to detect incidents that led to the crime and identify the criminals behind them as well.

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The model used is based on the detection theory is dependent on the ratio of how often an outcome is possible after giving a certain amount of data, like the probability of a mail being a spam as compared to a mail that is important.

Classifiers can be used in models to find if data belongs to one set or the say. Say for instance in the case of emails, whether the mail is spam or normal. Because of its similar areas of learning predictive analysis is almost similar to machine learning. That is why when predictive modeling is deployed in commercial environment it is known as predictive analysis.

Predictive analytics can therefore help to optimise marketing campaigns but it is difficult to see their benefits beyond the. This makes predictive analysis close to impossible to implement predictive analysis techniques with have a good and comprehensive understanding about industry. That is why the best way in which to benefit from predictive analysis is to learn the basics of the industry.

  • Predictors can help brands to rank their customers in a comprehensive manner: The central building block of any predictive analytic method is a predictor. For instance, recency is a predictor based on the amount of time since the said consumer has purchased a product/service of the brand. The more recent the consumer, the higher the value of their recency. A reliable campaign response predicator, consumers with higher recency will have greater chance of call back. This means that if the customer has recently purchased your product/service then they have better chances of giving you constructive feedback. In short, for every single prediction goal, there will be multiple predictors that can be used to rank the database of customer. For instance through predictors, brands can study the online behaviour of their customers. Those who spend less time online are not interested in extending their online subscription. By targeting customers who are more frequently online, brands can effectively maximise their resources in an effective manner.
  • Combining predictors can result in smarter rankings:Brands can create a model by bunching together multiple predictors.Creating a model is the main idea behind predictive analysis. One of the way in which two predictors can be combined is by simply adding them. So if both interest and time spent online can influence the chances of responding to a mailer, then a good predictor can be created by adding time spent online and interest. Such a scheme that is created by pulling together two predictors is thereafter known as a model and in the above case it is a linear model. That is why predictive analysis is sometimes called predictive modeling. At the same time, it is important to remember that in order to understand the complex nature of the market, predictive models will not be simple but really rich and complex and above all involve a lot of predictors.

Another aspect to keep in mind is that because there are so many predictive options available in the market, it becomes difficult to choose the correct one. With multiple formulas and industry complexity, it is close to impossible for brands to try them all in order to decide the best model.

Models of predictive analysis can be created on the computer as well where the organisation’s collective experience can be used understanding complex consumer behaviour and demographics. This is at the core a mixture of crunching as well as trial and error. Predictive analysis can be highly complex on one and very simple on the other hand, but it is important to remember that simple models may not be able to predict as well as the complex ones.

In conclusion, it is always better that a brand invests in a mutual models that is better able to predict customers and their behaviours. So while predictive analytics is based on automatic machine skills, the skills needed to drive them are human and therefore every brand must invest in both predictive analysis vs forecasting in a successful fashion.

Predictive Analysis vs Forecasting – How can it help companies?

Forecasting is a method by which companies find out trends that will dominate the market in the company years. It has many advantages not just for new startups but for established and old companies. Forecasting is defined as a planning tool that can help the management to cope with an uncertain future, mainly through the use of past data and analysis of market trends. The process of forecasting begins with certain assumptions that are based on the management experience, knowledge and astute judgement sense of the management team. These estimates are then projected on techniques like Box-Jenkins models, Delphi method, exponential smoothing, moving averages, regression analysis, and trend projection. Since any error in the assumptions will also result in a similar or magnified error in forecasting results, the technique of sensitivity analysis is used where a range of values is assigned to uncertain factors, which are also called variables.

4 major benefits of forecasting are as follows

  1. forecasting helps in establishing new startups and promoting new brands: Forecasting is an important element when new brands are being set up in the industry. This is especially true when the industry is filled with multiple challenges and there are many hurdles in the path of seeing up a successful brand. Forecasting can help entrepreneurs to find out the best way that they can overcome these challenges and thereby establish a successful company. Through forecasting brands can understand how they will be perceived in the market and whether their products have the capability to meet the expectations and demands of the target audience. In short, good and strong forecasting can help startup companies to increase their chances of success by helping them plan and strategise their entry in a much better manner. At the same time, good forecasting can help new brands to meet the supply and demand situation, thereby increasing their brand power and loyalty.
  1. Forecasting can help brands to use their financial resources in a much better manner, than before: Financial concerns, especially for new and small companies is a very important aspect. That is why it is important that in such situations, the available resources are utilised in a proper and effective manner. As no brand can survive without adequate capital, financial forecasting plays a very important role in such a scenario. By helping companies to divide their resources in a proper manner, financial forecasting can hold the key to proper and effective financial planning in a company.
  2. Forecasting can help the administration take good and successful management decisions: Every company is based on good administrative decisions. Without a strong administrative backbone, companies will completely turn into a failure, sooner or later. The administration team of any company is essentially a decision making process and has responsibility for making decisions and for ascertaining that the decisions made are carried out. That is why it is important that the wheels of the administrative department is working in a continue manner and it is here that forecasting plays a very important role as it helps companies to take decisions at the right time.
  3. Forecasting helps companies to plan in a systematic manner:Planning is a very important component of any company, be it in the long term or short term. Forecasting can help companies to plan their growth strategy while keeping in mind the needs of the consumers while at the same time having an intricate understanding of the market trends as well. In other words, good and proper planning whether it is for the overall growth of the company or for a section of the company is completely dependent on good forecasting techniques.

A conclusion of Predictive Analysis vs Forecasting

In the end, both Predictive Analysis vs Forecasting are two techniques through which brands can correctly forecast and understand market techniques while at the same time meet customer expectations as well. In short, the need today is not for better Predictive Analysis vs Forecasting methods, but for better application of the techniques at hand.

Machine Learning Training (17 Courses, 11+ Projects)


This course focuses on the most popular business forecasting methods: regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It also discusses enhancements such as second-layer models and ensembles, and various issues encountered in practice.

Learning Outcomes

This class teaches students how to:

  • Visualize time series data
  • Understand the different components of time series data
  • Distinguish explanation from forecasting
  • Specify appropriate metrics to assess forecasting models
  • Use smoothing methods with time series data (moving average, exponential smoothing)
  • Adjust for seasonality
  • Use regression methods for forecasting
  • Account for autocorrelation
  • Distinguish real trend and patterns from random behavior

Who Should Take This Course

Data Scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts, risk managers, anyone who needs to produce, interpret or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.


Dr. Galit Shmueli

Course Syllabus

Week 1

Characterizing Time Series and the Forecasting Goal; Evaluating Predictive Accuracy and Data Partitioning

  • Visualizing time series
  • Time series components
  • Forecasting vs. explanation
  • Performance evaluation
  • Naive forecasts

Week 2

Smoothing-based Methods

  • Model-driven vs. data-driven methods
  • Centered and trailing Moving Average (MA)
  • Exponential Smoothing (simple, double, triple)
  • De-trending and seasonal adjustment
  • Differencing

Week 3

Regression-based Models

  • Overview of forecasting methods
  • Capturing trend seasonality and irregular patterns with linear regression
  • Measuring and interpreting autocorrelation
  • Evaluating predictability and the Random Walk
  • Second-layer models using Autoregressive (AR) models

Week 4

Forecasting in Practice

  • Forecasting implementation issues (automation, managerial forecast adjustments, and more)
  • Communicating forecasts to stakeholders
  • Overview of further forecasting methods (neural nets, ARIMA, and logistic regression)
  • Forecasting binary outcomes

Class Dates

Jul 10, 2020 to Aug 7, 2020

Nov 13, 2020 to Dec 11, 2020

Mar 12, 2021 to Apr 9, 2021

Jul 9, 2021 to Aug 6, 2021

Nov 12, 2021 to Dec 10, 2021

No classes scheduled at this time.


There are no prerequisites for this course.

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

What Our Students Say​

The TA was really helpful and very responsive to questions. Overall the course was a great experience.

Considering all of the material that needed to be covered, I thought the course was well written and thought provoking.

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit:

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

Predictive Analytics 1 – Machine Learning Tools

Predictive Analytics Preview – 1 Week Trial Course

Regression Analysis

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.


Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software and guided data modeling problems using software.

In addition to assigned readings, this course also has an end of course data modeling project.

Course Text

“Practical Time Series Forecasting” in eBook or hardcopy, or, if you are using R, “Practical Time Series Forecasting in R.” Those in South Asia can purchase the books online here.


This is a hands-on course, and, while any software capable of doing time series forecasting can be used, assignment support is offered for two programs:

1. XLMiner, a data mining program available either (a) for Windows versions of Excel or (b) over the web. Course participants will have access to a low-cost license for XLMiner.

2. R, a free statistical programming environment.

Be sure to choose the book that corresponds to your chosen software program.

For XLMiner users: Course participants will have receive a low-cost license for XLMiner – this is a special version, for this course. Do NOT download the free trial version of XLMiner from as it may conflict with the special course version.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?”

Analytics and forecasts

Moody’s Analytics provides comprehensive economic data and forecasts at the national and subnational levels. We cover more than 180 countries/jurisdictions, including more than 2,000 regions in Europe and all U.S. states, metropolitan areas and counties. Our databases contain more than 280 million time series. This section provides information on economic data solutions. Visit to view all data solutions.

Most of our historical data series can be purchased with a credit card at our online store. Our forecast data can be purchased through a subscription by contacting us. We offer several methods for data retrieval, including a flexible web-based platform, a Microsoft Excel Add-In and a API. We also can deliver data and forecasts to you via custom delivery.


Moody’s Analytics enhances its historical databases by including value-added series and historical estimates to address limitations in the raw as-reported data. These can include short history, low frequency, long lag, limited geographic or industrial granularity, and changes in definitions or classifications. Our estimates and value-added series synthesize reported data to overcome these limitations, simplifying macroeconomic analysis and facilitating cross-country comparisons and local analysis. We ensure high quality by using nationally sourced data wherever possible, supplemented by multinational datasets as needed.


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