How to Forecast Sales Using Prophet and Python
Learn to build accurate sales forecasts using Prophet and Python in just 15 lines of code. Discover how to leverage this powerful tool for various time series predictions, from market trends to weather forecasting.
Time Series Analysis for Beginner Data Scientists - Time Series Forecasting
Learn time series analysis for data science: navigate, analyze, and model time series data, create forecasts, and build interactive dashboards. Ideal for beginners with basic coding skills.
Customizing Game Experiences with Lifetime Value Models and Predictive Analytics
Customize game experiences using lifetime value models and predictive analytics. Learn from Space Ape Games' strategies to boost retention and monetization in mobile gaming.
Time Series and Biological Network Analysis via Directed Graphs
Explore robust techniques for analyzing biological time series data using directed acyclic graphs, focusing on extremal events and applications in genomic research and network analysis.
Visual Pattern Exploration At and Across Scales
Innovative visualization systems for exploring patterns in large-scale datasets, combining interactive visual techniques with machine learning to enhance data analysis in genomics, geospatial, and time-series fields.
Data Analysis Using Zigzag Persistence for Temporal Hypergraphs
Explore temporal hypergraphs and zigzag persistence in Topological Data Analysis to understand complex systems' evolution, with applications in cybersecurity and social networks.
AI at Scale to Understand Impact of Weather on Businesses
Explore how AI scales to analyze weather's impact on business, leveraging historical data to predict demand and inform decision-making across industries.
Detecting Non-Trivial Cycles of Point Clouds and Time Series Data on Manifolds
Explore techniques for identifying complex cycles in point clouds and time series data on manifolds, with applications in machine learning and dynamical systems.
Learning and Exploiting Statistical Dependencies in Networks - 2007
Explore statistical dependencies in networks, focusing on learning joint distributions, network navigation, and structure indexing. Discover applications in web mining, bioinformatics, and fraud detection.
Learning and Mining in Large-Scale Time Series Data - 2015
Explore machine learning approaches for analyzing large-scale time series and spatio-temporal data, with applications in healthcare and climate modeling.
Transformers in Time Series: A Survey
Explore the application of Transformer models in time series analysis, covering recent advancements and potential future directions in this rapidly evolving field.
Smoothing Crypto Time Series with Wavelets - Real-world Data Project
Explore real-world data science through crypto time series smoothing. Learn moving averages, polynomial fits, Fourier transforms, and wavelet decomposition techniques for financial data analysis.
Auto-regressive Approximations to Non-stationary Time Series: Inference and Applications
Explore auto-regressive approximations for non-stationary time series, covering inference methods and practical applications in forecasting, estimation, and resampling.
One Analysis a Day Keeps Anomalies Away - Detecting and Analyzing Data Anomalies
Explore Python techniques for data observability, anomaly detection, and root cause analysis in multivariate time series. Learn to distinguish anomalies from data drift and turn insights into opportunities.
ML Monitoring ML: Scalable Monitoring of ML Models in Production Environments
Explore scalable ML model monitoring in production. Learn key performance measures, anomaly detection techniques, and an open-source agent for automated tracking and alerting of model issues.
MLOps at Scale: Predicting Bus Departure Times Using 18,000 ML Models
Explore MLOps at scale through a case study on predicting bus departure times using 18,000 ML models. Learn practical insights for implementing large-scale machine learning projects.
Functional Data Analysis in Practice
Explore practical applications of functional data analysis with insights from Qiwei Yao of the London School of Economics, covering key concepts and methodologies.
AutoML Time Series Forecasting for Infrastructure Resources
Spark-based AutoML framework for scalable time series forecasting of IT infrastructure resources, enabling efficient long-term planning and daily dynamic provisioning.
Cross-Validation Revisited: Evaluating Machine Learning Models with Non-IID Data
Explore challenges and techniques for evaluating machine learning models with non-IID data, focusing on spatial autocorrelation, concept drift, and phylogenetic non-independence.
Coresets for Robust Submodular Maximization with Deletions
Explore submodular maximization against deletions, learning about coresets and algorithms for robust optimization in data science problems. Gain insights from Nikolaj Tatti's research on streaming and offline approaches.
Machine Learning Analysis of Chaos and Vice Versa - Edward Ott, University of Maryland
Exploring machine learning's effectiveness in analyzing chaotic systems and vice versa. Covers short-term forecasting, ergodic properties, Lyapunov exponents, and the interplay between chaos theory and ML in understanding complex dynamical systems.