# Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series

arXiv: https://arxiv.org/abs/2106.10952 · HTML: https://mlsanity.com/paper/2106.10952/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models](https://mlsanity.com/paper/2005.10111/) (2020)
- [LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory](https://mlsanity.com/paper/1909.06041/) (2019)

## Built on by (most cited first, 4 of 4)

- [Neural Contextual Anomaly Detection for Time Series](https://mlsanity.com/paper/2107.07702/) (2021)
- [Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting](https://mlsanity.com/paper/2111.06581/) (2021)
- [Monte Carlo EM for Deep Time Series Anomaly Detection](https://mlsanity.com/paper/2112.14436/) (2021)
- [C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting](https://mlsanity.com/paper/2312.15002/) (2023)

## Abstract (arXiv)

This work proposes a novel method to robustly and accurately model time series with heavy-tailed noise, in non-stationary scenarios. In many practical application time series have heavy-tailed noise that significantly impacts the performance of classical forecasting models; in particular, accurately modeling a distribution over extreme events is crucial to performing accurate time series anomaly detection. We propose a Spliced Binned-Pareto distribution which is both robust to extreme observations and allows accurate modeling of the full distribution. Our method allows the capture of time dependencies in the higher order moments of the distribution such as the tail heaviness. We compare the robustness and the accuracy of the tail estimation of our method to other state of the art methods on Twitter mentions count time series.