Common issues with Time Series data and how to solve them
Time-series data is all around us: from logistics to digital marketing, from pricing to stock markets. It’s hard to imagine a modern business that has no time series data to forecast. However, mastering such forecasting is not an easy task. For this talk, together with other domain experts, I have collected a list of common time series issues that data professionals commonly run into. After this talk, you will learn to identify, understand, and resolve such issues. This will include stabilising divergent time series, organising delayed / irregular data, handling missing values without anomaly propagation, and reducing the impact of noise and outliers on your forecasting models.
Affiliation: Xebia Data
Vadim Nelidov is a Lead Data Science consultant at Xebia Data with diverse experience in the data domain in a variety of industries from energy sector and banking to skincare and agriculture. Throughout his years in the data world, Vadim has been combining advanced data science with business insights to make data work with an impact. He aspires to see far beyond what is on the surface and get to the essence of the problems, discovering robust and scalable long-term solutions rather than temporary fixes.
Vadim is passionate about sharing his knowledge and insights, believing that Data literacy should not be a privilege of a few. And his goal is to be there to make this a reality. Making the intricacies of data science intelligible and uncovering the regularities hiding in the data is a major source of inspiration for Vadim. With this goal in mind, he combines his years of experience in consulting with his background in statistics, research and teaching to make this knowledge accessible to businesses and individuals in need.