Fourier Phase Index for Extracting Signatures of Determinism and Nonlinear Features in Time Series
Explore a novel method for analyzing time series data, extracting deterministic signatures and nonlinear features using the Fourier Phase Index.
Forecasting Online Demand for Labour Planning
Learn to forecast online sales demand using data science for accurate resource planning in fulfilment centres, reducing costs and improving delivery times.
AI Demand Forecasting: Building a Scalable Machine Learning System for Thousands of Products
Explore ASOS's journey in building a scalable AI demand forecasting system, addressing challenges in ML modeling, data processing, and integration for improved stock management.
Beyond Standard Deep Learning Models for Time Series and Sequences
Explore advanced techniques for temporal data analysis, including confident predictions and adaptive computational time models for improved sequence-to-sequence processing.
Engineering and Production Techniques for Managing Feature Drift in AI Models
Explore engineering techniques for managing feature drift in large-scale AI models, drawing insights from real-world case studies and best practices.
A Cookbook for Deep Continuous-Time Predictive Models
Explore deep continuous-time predictive models for irregularly-sampled time series, from feedforward approaches to latent-variable stochastic differential equations.
A Tale of A Thousand Pipelines: Building Scalable Deep Learning Pipelines for Time-Series Datasets
Discover scalable deep learning pipelines for massive time-series datasets, reducing costs by 80% while training hundreds of models efficiently.
Forecasting the Future with EarthPT
Explore EarthPT, an open-source Earth Observation pretrained transformer. Learn about its capabilities in predicting surface reflectances, vegetation indices, and potential applications in land use classification and crop yield forecasting.
Fast Fourier Transform (FFT) of Time Series in Kafka Streams
Explore digital signal processing with Kafka Streams, transforming time-domain signals to frequency domain using FFT. Learn to build a precise frequency alert system and visualize results in real-time.
Anomaly Detection on Time Series Data Using Apache Flink
Implement real-time anomaly detection on network activity time series data using Apache Flink. Compare EWMA and PEWMA algorithms for effective intrusion detection in cloud-native deployments.
Mixing Properties of (Non-)Stationary INGARCH(1,1) Processes
Explore mixing properties of (non-)stationary INGARCH(1,1) processes in this mathematical conference talk, focusing on adaptive and high-dimensional spatio-temporal forecasting methods.
Cycles, Phase Analysis, and Synchronization in Ecological Populations - A Tale of Population Dynamics
Explore ecological population dynamics through cycles, phase analysis, and synchronization. Gain insights into predator-prey relationships, mathematical models, and data analysis techniques.
Frontiers of Foundation Models for Time Series
Explore foundation models for time series data, addressing challenges in science applications and discussing future research directions in deep learning for time series analysis.
MLOps MLflow: Time Series Forecasting with SkTime and Prophet
Explore time series forecasting using SkTime and Prophet libraries in MLflow, enhancing your MLOps skills with practical implementation.
Scalable Inference of Dynamic Graphical Models with Combinatorial Structures
Explore scalable inference techniques for dynamic graphical models with combinatorial structures, focusing on industrial and operations engineering applications.
Towards Robust and Efficient AI at Scale
Explore robust, efficient AI for scientific time series forecasting. Learn scalable transformer architectures, energy optimization, and balancing performance with sustainability in large-scale AI applications.
Time Series Analysis - Class 4
Explore advanced time series analysis techniques in socio-economic complexity, focusing on nonlinear systems applications and cutting-edge research insights.
Time Series Analysis Tools with Applications to Neuroscience - Class 1
Explore time series analysis tools and their applications in neuroscience, focusing on nonlinear dynamics and complex networks in brain function.
Time Series Analysis Tools with Applications to Neuroscience - Class 2
Explore time series analysis tools and their applications in neuroscience, focusing on advanced techniques for understanding complex neural data.
Information Theory Tools for Neuroscience Applications - Class 1 of 3
Explore information theory tools for neuroscience, covering dynamical systems, chaos, complexity measures, and probability distributions in this comprehensive lecture.
Information Theory Tools for Neuroscience Applications - Class 2
Explore information theory tools for neuroscience, focusing on advanced concepts and practical applications in complex neural systems analysis.