ICAI: The labs – AI & the Service Industry in NL
This ICAI the Labs session is focused on AI and the Service Industry in The Netherlands. AI for Fintech lab and AIRlab Amsterdam share their story. Two speakers highlight their recent work at the intersection of AI and the service industry. And two leaders in the field discuss the field more broadly.
12.00 (noon): Arie van Deursen (TU Delft) presents the AI for FinTech Lab
12.05: Elvan Kula on Using Machine Learning to Improve On-Time Software Delivery
12.25: Sebastian Schelter (U. Amsterdam) presents AIRLab Amsterdam
12.30: Olivier Sprangers on Parameter Efficient Deep Probabilistic Forecasting
12.50: Q&A with Arie van Deursen and Sebastian Schelter
Speaker: Elvan Kula, AI for FinTech Lab, TU Delft
Title: Using Machine Learning to Improve On-Time Software Delivery
Abstract: To be supplied.
Speaker: Olivier Sprangers, AIRLab Amsterdam, U. Amsterdam
Title: Parameter Efficient Deep Probabilistic Forecasting
Abstract: Probabilistic time series forecasting is crucial in many application domains such as retail, ecommerce, finance, or biology.
With the increasing availability of large volumes of data, a number of neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art performance on real-world benchmarks. However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models.
To address this problem, we introduce a novel Bidirectional Temporal Convolutional Network (BiTCN), which requires an order of magnitude less parameters than a common Transformer-based approach.
Our model combines two Temporal Convolutional Networks (TCNs): the first network encodes future covariates of the time series, whereas the second network encodes past observations and covariates.
We jointly estimate the parameters of an output distribution via these two networks.
Experiments on four real-world datasets show that our method performs on par with four state-of-the-art probabilistic forecasting methods, including a Transformer-based approach and WaveNet, on two point metrics (sMAPE, NRMSE) as well as on a set of range metrics (quantile loss percentiles) in the majority of cases. Secondly, we demonstrate that our method requires significantly less parameters than Transformer-based methods, which means the model can be trained faster with significantly lower memory requirements, which as a consequence reduces the infrastructure cost for deploying these models.
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