Exploring Time Series Analysis:From Regression Models to Real-World Applications

Abstract

Unlock the hidden secrets in ordered historical data with time series analysis. This talk will cover the definition and importance of time series analysis, the goals of characterising underlying trends and patterns, as well as forecasting future values. We'll delve into linear and non-linear regression models for time series analysis and give real-world examples of analysing air quality data from Indian cities and stock price data.

You'll also learn about the limitations of time series analysis and discover a new open-source framework for comparing and benchmarking these models against different datasets. Whether you're a data scientist, a financial analyst or simply curious about this area, join us for an engaging and educational journey into the world of time series analysis.

Bio of Speaker

Frank is a Principal Data Scientist and Solutions Consultant at Sahaj.ai, with extensive experience in diverse industries ranging from aerospace, shipping, and offshore oil & gas to tech startups and smart cities. As a respected organiser at PyData Bristol, Frank is passionate about empowering data scientists and machine learning engineers through knowledge sharing and community building. Outside of his work, Frank's dedication to sustainability is reflected in his innovative data-driven side projects that aim to address environmental issues.

Pre-requisites for Attendees

No prior knowledge is expected of the topic, though it is recommended to have a basic understanding of the Python programming language, statistics, and mathematics.

Take aways from the Talk

This talk is a great opportunity for anyone interested in data analysis, particularly in the field of time series analysis. Attendees will learn about the definition and importance of time series analysis, the goals of characterising underlying trends and patterns, as well as forecasting future values. The talk will cover both linear and non-linear regression models for time series analysis, and real-world examples will be provided using air quality data from Indian cities and stock price data.