## Recently Published

##### Intro to janus

janus is a coarse-to-fine optimization for a recommending system based on embedding neural networks

##### Introduction to codez

Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence, based on Tensorflow.

##### Introduction to segen

segen is a model for sequence generalization using the “network” of similarities among sequences for the extrapolation

##### Intro to naive

Naive is a model to extract the common patterns from sequences and extrapolate time features

##### Introduction to dymo

dymo proposes an implementation of Dynamic Mode Decomposition (SVD-approach) to predict multiple time features

##### Introduction to AUDREX

Dynamic regression for time series using Extreme Gradient Boosting with hyper-parameter tuning via Bayesian Optimization or Random Search.

##### Introduction to Jenga

Automatic fast extrapolation of time features using kNN.

##### Introduction to spooky

Time features extrapolation through spectral analysis and jack-knife resampling.

##### Tetragon: a brief introduction

Model for sequence forecasting based on expansion of distance matrix.

##### Introduction to SNAP

SNAP is a tool to design deep neural network with a single line of code.

##### Proteus: a brief introduction

Proteus is a Sequence-to-Sequence Variational Model for time-feature analysis based on a wide range of different distributions.