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.
This talk will walk you through 4 common issues with Time Series and illustrate them using
the context of energy demand forecasting. For each of these issues you will learn to identify,
understand, and resolve them better. These issues are time series instability, delayed and
irregular time series data, hard-to-impute missing values, impact of noise and outliers on
forecasting models. The talk is therefore split into 4 parts each with some room for
questions. Each part will provide some high-level background, explanations, examples and
code snippets, while avoiding unnecessary in-depth computations and formulas. Therefore,
the whole talk is accessible to both specialists with experience in Time Series analytics as
well as those without such experience who nonetheless intend to broaden their
understanding of this field and gain some valuable insights for the business problems that
they are likely to encounter in the future.
Data Scientists / Analysts working with time series data and understanding at least the
basics of Pandas / Scikit-learn Python libraries as well as what a time series forecasting
problem entails would benefit the most from this talk. However, other less technical
specialists (management, product owners etc.) can still gain valuable domain knowledge in
this field.
