classification,imputation,long term forecast和short term forecast,long term指的是预测series在96-720之间,short term指的是预测series在6-48之间,对于每个方向,里面脚本按<模型名 +数据集名>组织,比如Informer_M4.sh,是在M4 dataset上应用Informer模型的脚本。
Time-Series-Library 是由 THUML 团队开发的一个 Python 库,旨在简化和加速时间序列数据的预处理、建模与评估过程。它集成了多种先进的时间序列模型,如 ARIMA、Prophet 和 LSTM,并提供了易用的 API,使得数据科学家和开发者能够快速实现其项目需求。 1.2 技术分析 模块化架构: Time-Series-Library 的设计遵循模块化...
时间序列(time series)是一系列有序的数据。通常是等时间间隔的采样数据。如果不是等间隔,则一般会标注每个数据点的时间刻度。 time series data mining 主要包括decompose(分析数据的各个成分,例如趋势,周期性),prediction(预测未来的值),classification(对有序数据序列的feature提取与分类),clustering(相似数列聚类)等。
This library is constructed based on the following repos: Forecasting:https://github.com/thuml/Autoformer. Anomaly Detection:https://github.com/thuml/Anomaly-Transformer. Classification:https://github.com/thuml/Flowformer. All the experiment datasets are public, and we obtain them from the follo...
ETSC is a Python Early Time-Series Classification library for public use, used in "A Framework to Evaluate Early Time-Series Classification Algorithms", Authors: Charilaos Akasiadis, Evgenios Kladis, Petro-Foti Kamberi, Evangelos Michelioudakis, Elias Alevizos, Alexander Artikis. Cite as: Akasiad...
Deep learning models have been shown to be a powerful solution for Time Series Classification (TSC). State-of-the-art architectures, while producing promising results on the UCR and the UEA archives, present a high number of trainable parameters. This can lead to long training with high CO2 ...
Time Series Data Library now on DataMarket Hyndsight 被引量: 0发表: 0年 Mining Time Series Data Some of the problems raised by massive time series databases are briefly considered. They include indexing, clustering, classification, prediction, associa... EJ Keogh - American Cancer Society 被引...
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time ...
Thus, the SLEX library has the ability to extract local spectral features of the time series. The first step in our procedure, which is the feature extraction step based on Saito (1994), is to find a basis from the SLEX library that can best illuminate the di#erence between two or more...
主要逻辑在Time-Series-Library/models/TimesNet.py文件里Model::classification函数,代码中是跑的EthanolConcentration数据集,该数据集序列长度1751,输入维度3,经过编码成32维,batch是16。 231 def classification(self, x_enc, x_mark_enc): 232 # embedding ...