Data Science
Trend Forecast Service
Enabling data-driven decision making Enabling data-driven decision making
Empirical evidence alone leave blind spots

Empirical evidence is an essential part of decision making, albeit the market is complex system to an extend which is far beyond what most humans are capable of processing. Even those with deep knowledge and experience often have little choice but to focus on myopic perspectives that incorporate a limited range of factors. Whether we realize it or not, we are drowning in a sea of complex systems. By analyzing a myriad of real-time online information, TFS identifies what matters most to the core of your organization and enhance performance along with reducing the risk of drowning in the data flow.

It is not enough to know the current market situation

As the act of creation is singular, every moment in business happens only once. The next Bill Gates will not build an operating system and the next Satoshi Nakamoto will not an create electronic peer to peer cash system. In this ever-changing world, it is not enough to know what consumers are thinking now. We need foresight on what consumers will do next and where the market moves. TFS evaluates trends by observing future growth potential at scale for any domain with incremental efficiencies to bring intelligence for a data driven decision-making process.

It is not enough to know how the market is right now. TFS quickly and accurately enables trends to be ranked objectively based on real consumer conversation patterns and machine learning. By analyzing a myriad of unstructured text and incorporating big data analysis, TFS helps any organization to analyze and formularize movements.
Analysis for Trend Potential Value
Impact study of predicting manga title
Manga titles with high trend potential value were detected and presented as titles that will be popular in the future. About six months later, the title became a magazine cover and almost a year after that it became an anime.
Impact study of predicting online novel
Scoring and ranking of all tiles of online novel posting sites
Related Papers
Machine learning model with human cognitive bias that can be learned from small, biased data sets
Implementation and application of human cognitive bias to neural networks
Construction of user recommendation system based on sentence similarity DVM (Direct Vector Matching)