The goal of predictive analysis is to develop predictions for the development of complex, multifaceted processes in various fields of science, industry, economy or other spheres of human activity. In addition, predictive analysis may refer to objectively performing processes such as natural phenomena, climate change, geological, cosmic etc.
Predictive analysis should be based on taking into account in the analytical methodology possible the most modern prognostic models and a large amount of data necessary to perform the most accurate predictive analysis. In this way, the result of the prediction analysis performed will be the least subject to the risk of analytical error, ie an incorrectly designed forecast.
Predictive analysis can be improved by using computerized modern information technologies, which include computing in the cloud of large data sets stored in Big Data database systems. In the predictive analysis, Business Intelligence analytics and other innovative information technologies typical of the current fourth technological revolution, known as Industry 4.0, can also be used.
The current technological revolution known as Industry 4.0 is motivated by the development of the following factors:
Big Data database technologies, cloud computing, machine learning, Internet of Things, artificial intelligence, Business Intelligence and other advanced data mining technologies. On the basis of the development of the new technological solutions in recent years, dynamically developing processes of innovatively organized analyzes of large information sets stored in Big Data database systems and computing cloud computing for the needs of applications in such areas as: machine learning, Internet of Things, artificial intelligence, Business Intelligence are dynamically developing.
For the abovementioned application examples, one can add predictive analyzes of subsequent, other fields of application of advanced technologies for the analysis of large data sets such as Medical Intelligence, Life Science, Green Energy, etc. Processing and multi-criteria analysis of large data sets in Big Data database systems is carried out according to V4 concepts, ie Volume (meaning a large number of data), Value (large values of certain parameters of the analyzed information), Velocity (high speed of new information) and Variety (high variety of information).
The advanced information processing and analysis technologies mentioned above are used more and more often for the needs of conducting predictive analyzes concerning, for example, marketing activities of various business entities that advertise their offer on the Internet or analyze the needs in this area reported by other entities, including companies, corporations, institutions financial and public. More and more commercial business entities and financial institutions conduct marketing activities on the Internet, including on social media portals.
More and more public institutions and business entities, including companies, banks and other entities, need to conduct multi-criteria analyzes on large data sets downloaded from the Internet describing the markets on which they operate, as well as contractors and clients with whom they cooperate.
On the other hand, there are already specialized technology companies that offer this type of analytical services, including offering predictive analysis services, develop custom reports, which are the result of multicriteria analyzes of large data sets obtained from various websites and from entries and comments. contained on social media portals based on sentiment analyzes of the content of entries in the comments of Internet users.
Do you agree with my opinion on this matter?
In view of the above, I am asking you the following question:
How can you improve the process of predictive analysis?
Please reply
I invite you to discussion and scientific cooperation
Dear Colleagues and Friends from RG
The key aspects and determinants of the applications of modern computerized information technologies for data processing in Big Data and Business Intelligence database systems for the purpose of conducting predictive analyzes are described in the following publications:
Article APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTEL...
Article The Big Data technologies as an important factor of electron...
Article The Technological Solutions Big Data and the Importance of B...
Article THE QUESTION OF THE SECURITY OF FACILITATING, COLLECTING AND...
I invite you to discussion and cooperation.
Best wishes
This question was already discussed in the thread following the previous post of this author regarding predicting the future using Big Data. Here the author confuses the terms 'prediction' and 'forecasting' using them interchangeably, such as in the statement"...the result of the prediction analysis performed will be the least subject to the risk of analytical error, i.e. an incorrectly designed forecast." However, prediction is usually reserved for a definitive and specific statement about the value of some variable at the specific time in the future. In contrast, forecasting is a probabilistic statement about a range of the possible future values of the variables.
Next, dynamic systems can be chaotic or stochastic. Chaotic systems can be deterministic and predictable. In this case the value of variable X at a given time can be used to predict the value of X in a long time in the future. Example is Newtonian dynamics that can predict accurately solar eclipse or other events in the universe 1000s years ahead, and others. However, a chaotic system can be deterministic and not predictable in the long run, i.e. the value X can be predicted only for a very short time ahead. The reason for this is high sensitivity to initial conditions that amplifies the unavoidable inaccuracy of the initial conditions. An example is impossibility of a long-term weather forecasting that was originally discovered by the famous Lorenz system in 1963 and is widely known in the literature. The profound fundamental consequence is that regardless of the computer power and amount of collected data (Big Data) the long-term weather forecast as well as forecasting of many complex non-linear economic, geological, etc. systems will never be reliable, although the short-term forecasting accuracy can be somewhat and slowly improved with the increase of computer power. A well-known simple example of a chaotic deterministic but unpredictable system is the non-linear time series equation X(n+1)=A*X(n)[1-X(n)] that behaves totally unpredictable if the numerical coefficient A>=3.95.
I see the question from its real life application point of view and I'll discuss the "Predictive Analytics" in Business. Yes, it's a common problem and it was discussed many times, but it does not mean that we cannot talk about this again.
One successful approach to improve Predictive Analytics (I know that "prediction" and "forecasting" are not exactly the same, for example see my book, but many people don't care about this and use both of them as synonyms) are the Statistical Learning Networks that address in very cost-effective way many issues in model building and forecasting, such as overfitting, small sample size, forecast accuracy etc. For more information you can download the following papers (both of them available here on Reseacrhgate web site: SELF-ORGANIZING DATA MINING TECHNIQUES IN MODEL BASED SIMULATION GAMES FOR BUSINESS TRAINING AND EDUCATION and Statistical Learning Networks in Simulations for Business Training and education.
Last but not least, if we stop discussing something it will never be improved further more, and, please, don't tell me that everything in Predictive Analytics (Analysis) has been fixed yet...
In my opinion, the development of ICT and advanced information processing technologies in the scope of continuing the current technological revolution Industry 4.0 can significantly increase the possibilities of improving predictive analyzes.
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