All Posts By

Jingsong Cui

The Amazing Race of Forecast Models

By | clypd Blog | No Comments

Deal or No Deal

To plan for a TV advertising campaign, it is important to have a good understanding of the trend in TV audiences: who will be watching what, when, and using what screen. An accurate and reliable forecast is an integral component of clypd’s advanced targeting platform. For this reason, the Data Science team at clypd is always looking for ways to improve our forecast models to be more useful, accurate and reliable.

In the last decade or so, through the help of more data and better technology, lots of new algorithms have been developed. My previous blog post talks about the benefits of using both statistics and machine learning models. Recently, the data science team held an internal competition to examine how various models pair up against each other. This post provides some details of the competition. Read More

Using Algorithms to Improve TV Audience Forecasting

By | clypd Blog, Research | No Comments

This is the first of a series of blogs around building time-series forecasting models. At clypd, we use forecasting models to help media owners and buyers forecast future TV audiences. A successful forecasting model depends on many factors. In this blog, we focus on algorithms, and how we tap into both modern Machine Learning (ML) models and classical statistics models to take advantage of what both offer.

The advancement of Machine Learning and Artificial Intelligence has been creating amazing stories everyday, from the AI assistant and self-driving vehicles to computer programs beating professional Go players. At clypd, we also have lots of success stories of using ML models. With the benefits of better accuracy and better automation, these ML models are an integral part of our forecasting models. At the same time, we also continue to find great value in “conventional” statistics models. So, instead of pitching Data Scientist vs. Statistician, let us look at ML models vs. statistical models, and how we can leverage both types of approaches in building a TV audience forecasting model.

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