Chronos: Learning the Language of Time Series

  • Chronos is a framework designed for pretrained probabilistic time series models.
  • It utilizes scaling and quantization to tokenize time series values into a fixed vocabulary.
  • Chronos trains transformer-based language model architectures (specifically, models from the T5 family with parameters ranging from 20M to 710M) using cross-entropy loss.
  • The models are pretrained on a mix of publicly available datasets and a synthetic dataset generated via Gaussian processes, enhancing generalization.
  • In a comprehensive benchmark involving 42 datasets, including both classical local models and deep learning approaches, Chronos models:
  • (a) significantly outperform other methods on datasets included in the training corpus;
  • (b) show comparable or occasionally superior zero-shot performance on new datasets compared to methods trained specifically on those datasets.
  • These results demonstrate the potential of pretrained models to leverage time series data across various domains for improving zero-shot accuracy on unseen forecasting tasks, suggesting a simplified approach to forecasting pipelines.

Category Encoders

A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques.

Category Encoders is a Python library for encoding categorical variables for machine learning tasks. It is available on and extends the capabilities of scikit-learn’s preprocessing module.

The library provides several powerful encoding techniques for dealing with categorical data, including:

  • Ordinal encoding: maps categorical variables to integer values based on their order of appearance
  • One-hot encoding: creates a binary feature for each category in a variable
  • Binary encoding: maps each category to a binary code
  • Target encoding: encodes each category with the mean target value for that category
  • Hashing encoding: maps each category to a random index in a hash table

Category Encoders also supports a range of advanced features, such as handling missing values, combining multiple encoders, and applying encoders to specific subsets of features.

Overall, Category Encoders is a useful tool for preprocessing categorical data and improving the accuracy and performance of machine learning models.

TabTransformer: Tabular Data Modeling Using Contextual Embeddings

The main idea in the paper is that the performance of regular Multi-layer Perceptron (MLP) can be significantly improved if we use Transformers to transforms regular categorical embeddings into contextual ones.

The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embed- dings of categorical features into robust contextual embed- dings to achieve higher prediction accuracy.