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Inherently Interpretable Time Series Classification via Multiple Instance Learning (MILLET)
qianlima-lab / SoftShape
Forked from ZLiu21/SoftShapeThis is an official pytorch implementation for paper "Learning Soft Sparse Shapes for Efficient Time-Series Classification" (ICML-25, Spotlight).
[VLDB' 25] ChatTS: Understanding, Chat, Reasoning about Time Series with TS-MLLM
This is an official pytorch implementation for paper "Learning Soft Sparse Shapes for Efficient Time-Series Classification" (ICML-25, Spotlight).
Source code for the AAAI 2025 paper "TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents."
Context is Key: A Benchmark for Forecasting with Essential Textual Information
[ICLR'24] Official PyTorch Implementation of ContraLSP
This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaof…
Interpretable and Steerable Sequence Learning via Prototypes
This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series.…
AERCA: Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery (ICLR 2025 Oral)
Automatic extraction of relevant features from time series:
Unified Model Interpretability Library for Time Series
NeurIPS 2024 (spotlight): A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis
Concept Bottleneck Models, ICML 2020
Implementation of the InterpretTime framework
An Open-Source Library for the interpretability of time series classifiers
A list of (post-hoc) XAI for time series
Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
Implementation codes of NeurIPS 2024 paper "Towards Understanding Evolving Patterns in Sequential Data"
A collection of Jupyter notebooks showing how to use the Qiskit SDK
Source content for the Qiskit Textbook
[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415