Fall 2023 - Montreal Time Series Meetup

We are calling all Time Series enthusiasts! Borealis AI and Moov AI present the Fall 2023 installment of our time series talks.

You're invited to our IN-PERSON meetup on Oct 12th from 5:30 pm - 7:30 pm ET. Join us at Borealis AI’s Office for networking, refreshments, and exciting research discussions.

You'll hear from two industry practitioners working at the forefront of Time Series research and business applications. There will be plenty of Q&A time where you can pick our speaker's brains.

Event Agenda:
*All times are in EST

• 5:30 pm - 6:00 pm - Sign in, Grab a name tag, refreshments + Network
• 6:00 pm - 6:05 pm - Welcome / Opening Remarks
• 6:05 pm - 6:35 pm - Jonathan + Q&A
• NETWORKING BREAK
• 6:55 pm - 7:25 pm - Shabam + Q&A
• 7:25 pm - Close - Networking and Wrap-up

Our Speakers:

Jonathan Guymont, Analyst, Data Science - CDPQ

• Talk: KNN-based forecasting of treasury rate variations

• Talk Abstract: The presentation will address how the K-nearest neighbours (KNN) algorithm can be used to forecast significant variations in the US Treasury rate. The results demonstrate the effectiveness of the KNN algorithm for forecasting the direction and magnitude of these variations and highlight the importance of carefully selecting the metric for feature selection, as it can significantly impact the performance of the model. The transparency of KNN is appreciated by stakeholders, but the trade-off between capacity and explainability must be handled appropriately.

Shubham Vashisth, PhD Candidate, McGill University

• Talk: KGFarm: A Holistic Platform for Automating Data Preparation

• Talk Abstract: Data preparation is critical for improving model accuracy. However, data scientists often work independently, spending most of their time writing code to identify and select relevant features and enrich, clean, and transform their datasets to train predictive models for solving a machine learning problem. Working in isolation from each other, they lack support to learn from what other data scientists have performed on similar datasets. This thesis addresses these challenges by presenting a novel approach that automates data preparation using the semantics of data science artifacts. Therefore, this work proposes KGFarm, a holistic platform for automating data preparation based on machine learning models trained using the semantics of data science artifacts captured as a knowledge graph (KG). These semantics comprise datasets and pipeline scripts. KGFarm seamlessly integrates with existing data science platforms, effectively enabling scientific communities to automatically discover and learn from each other’s work. KGFarm’s models were trained on top of a KG constructed from the top-rated 1000 Kaggle datasets and 13800 pipeline scripts with the highest number of votes. Our comprehensive evaluation uses 130 unseen datasets collected from different AutoML benchmarks to compare KGFarm against state-of-the-art systems in data cleaning, data transformation, feature selection, and feature engineering tasks. Our experiments show that KGFarm consumes significantly less time and memory compared to state-of-the-art systems while achieving comparable or better accuracy. Hence, KGFarm effectively handles large-scale datasets and empowers data scientists to automate data preparation pipelines interactively.

Location:
The Spring edition of the meetup will be taking place at Borealis AI's Montreal Office. At Borealis AI, we create real-world impact through scientific pursuit. Borealis AI researches, designs, and builds AI products and technologies that transform RBC businesses and shape the future of finance.

Address: O Mile-Ex, 6666 Rue Saint-Urbain, Suite 310, Montreal, QC H2S 3H1