Packages by Giancarlo Vercellino

CRAFT — 0.2.0

Conditional Regime Analog Forecasting with Trajectories

audrex — 3.0.0

Automatic Dynamic Regression using Extreme Gradient Boosting

chopper — 1.0

Changepoint-Aware Ensemble for Probabilistic Modeling

codez — 2.0.0

Torch Seq2Seq Encoder-Decoder Model for Time-Feature Analysis

dymo — 2.0.0

Dynamic Mode Decomposition Forecasting with Conformal Predictive Sampling

flowz — 0.1.0

Local Latent Flow Forecasting with Empirical Calibration

hmix — 1.0.3

Hidden Markov Model for Predicting Time Sequences with Mixture Sampling

janus — 1.0.0

Optimized Recommending System Based on 'tensorflow'

jenga — 2.0.0

Fast Extrapolation of Time Features using K-Nearest Neighbors

lambdaTS — 2.0.0

Variational Seq2Seq Model with Lambda Transformer for Time Series Analysis

naive — 2.0.0

Empirical Extrapolation of Time Feature Patterns

organik — 1.0.1

Multi-Horizon Probabilistic Ensemble with Copulas for Time Series Forecasting

proteus — 2.0.0

Multiform Seq2Seq Model for Time-Feature Analysis

rollcast — 0.1.0

Probabilistic Forecasting with Adaptive Mixtures of Rolling Statistics

segen — 2.0.1

Sequence Generalization Through Similarity Network

sense — 1.2.0

Automatic Stacked Ensemble for Regression Tasks

snap — 2.0.0

Simple Neural Application

spinner — 1.1.1

An Implementation of Graph Net Architecture Based on 'torch'

spooky — 2.0.0

Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling

temper — 1.1.1

Temporal Encoder-Masked Probabilistic Ensemble Regressor

tetragon — 1.3.0

Automatic Sequence Prediction by Expansion of the Distance Matrix

unfold — 1.0.1

Mapping Hidden Geometry into Future Sequences

wired — 1.0.1

Weighted Adaptive Prediction with Structured Dependence

xpect — 1.2

Probabilistic Time Series Forecasting with XGBoost and Conformal Inference