January 7, 2022 TIME SERIES

Python API for interacting with the Popcorn Time Servers

📝 CONTRIBUTIONS Before doing any contribution read CONTRIBUTING. 📧 CONTACT Email: [email protected] General Discord: https://discord.gg/dFD5HHa Developer Discord: https://discord.gg/rxNNHYN9EQ 📥 INSTALLING Latest PyPI stable release ⚙ HOW TO USE from popcorntime import PopcornTime popAPI = PopcornTime() 🤝 PARAMETERS CLASS PARAMETERS debug : bool, optional Enable for debug mode (Default: False) min_peers : int, optional Minimum number of peers to select torrent (Default: 0) min_seeds : int, optional Minimum number of seeds to select torrent (Default: 0) FUNCTION PARAMETERS FUNCTION set_logging_level level […]

October 15, 2021 TIME SERIES

Dynamical Wasserstein Barycenters for Time Series Modeling

Dynamical Wasserstein Barycenters for Time Series Modeling This is the code related for the Dynamical Wasserstein Barycenter model published in Neurips 2021. To run the code and replicate the results reported in our paper, # usage: DynamicalWassersteinBarycenters.py dataSet dataFile debugFolder interpModel [–ParamTest PARAMTEST] [–lambda LAM] [–s S] # Sample run on MSR data >> python DynamicalWassersteinBarycenters.py MSR_Batch ../Data/MSR_Data/subj090_1.mat ../debug/MSR/subj001_1.mat Wass # Sample run for parameter test >> python DynamicalWassersteinBarycenters.py MSR_Batch ../Data/MSR_Data/subj090_1.mat ../debug/ParamTest/subj001_1.mat Wass –ParamTest 1 –lambda 100 –s 1.0 The […]

February 24, 2021 Beginner, Career, Computer Vision, Data Exploration, Data Visualization, Deep Learning, Education, Excel, Interviews, Learning Path, NLP, Python, SQL, Statistics, Tableau, TIME SERIES Leave a comment

A Comprehensive Step-by-Step Guide to Become an Industry Ready Data Science Professional

Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. From face recognition cameras, smart personal assistants to self-driven cars. We are moving towards a world enhanced by these recent upcoming technologies. It’s the most exciting time to be in this career field! The global Artificial Intelligence market is expected to grow to $400 billion by the year 2025. From Startups to big organizations, all want to join […]

November 6, 2020 Beginner, Career, Computer Vision, Data Exploration, Data Visualization, Deep Learning, Education, Excel, Interviews, Learning Path, NLP, Python, SQL, Statistics, Tableau, TIME SERIES Leave a comment

A Comprehensive Step-by-Step Guide to Become an Industry-Ready Data Science Professional

September 28, 2020 Excel, Intermediate, Structured Data, Technique, TIME SERIES, Uncategorized Leave a comment

Time Series in Excel! Learn Exponential Smoothing Models for Time Series Forecasting in Excel

Overview Excel is the perfect fit for building your time series forecasting models We’ll discuss exponential smoothing models for time series forecasting, including the math behind them We’ll also implement these exponential smoothing models in MS Excel Introduction Time series in Excel – just seems like a natural fit, right? We see and design line charts in Excel all the time – from sales forecasts to revenue reviews – it all fits into how we think about using Excel […]

September 23, 2020 Machine Learning, TIME SERIES Leave a comment

Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change

Biology is both an important application area and a source of motivation for development of advanced machine learning techniques. Although much attention has been paid to large and complex data sets resulting from high-throughput sequencing, advances in high-quality video recording technology have begun to generate similarly rich data sets requiring sophisticated techniques from both computer vision and time-series analysis… Moreover, just as studying gene expression patterns in one organism can reveal general principles that apply to other organisms, the study […]

September 20, 2020 Machine Learning, TIME SERIES Leave a comment

Neural CDEs for Long Time Series via the Log-ODE Method

Neural Controlled Differential Equations (Neural CDEs) are the continuous-time analogue of an RNN, just as Neural ODEs are analogous to ResNets. However just like RNNs, training Neural CDEs can be difficult for long time series… Here, we propose to apply a technique drawn from stochastic analysis, namely the log-ODE method. Instead of using the original input sequence, our procedure summarises the information over local time intervals via the log-signature map, and uses the resulting shorter stream of log-signatures as the […]