• 时序资料汇总:模型和常见库对比


    Part1 领域介绍

    Time series is a series of data points indexed in time order.

    时间序列分析具体包括的任务:

    • 检索Indexing (query by content): given a time series and some similarity measure, find the nearest matching time series.

    • 聚类Clustering: find groups (clusters) of similar time series.

    • 分类Classification: assign a time series to a predefined class.

    • 分割Segmentation (Summarization): create an accurate approximation of a time series by reducing its dimensionality while retaining its essential features.

    • 预测Forecasting (Prediction): given a time series dataset up to a given time tn, forecast the next values.

    • 异常检测Anomaly Detection: find abnormal data points or subsequences.

    • 因果分析Rules Discovery: find the rules that may govern associations between sets of time series or subsequences

    推荐教材

    • Forecasting: Principles and Practice,第三版(英文),第二版(中文)

    推荐公开课

    • Intel 时间序列分析:讲授时间序列分析,以及用于预测、处理和识别顺序数据的方法。

    • 时间序列和平稳数据简介

    • 数据平滑化、自相关性和自回归积分滑动平均 (ARIMA) 模型等应用

    • 高级时间序列概念,如卡尔曼滤波器 (Kalman Filter) 和傅里叶变换 (Fourier Transformation)

    • 用于时间序列分析的深度学习架构和方法

    Part2 时序Python库

    在这里插入图片描述

    • Kats,推荐指数:⭐⭐

    • 主页:https://facebookresearch.github.io/Kats/

    • Github:https://github.com/facebookresearch/Kats

    • darts,推荐指数:⭐⭐

    • 介绍:a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks.

    • 主页:https://unit8co.github.io/darts/

    • Github:https://github.com/unit8co/darts

    • GluonTS,推荐指数:⭐⭐⭐⭐

    • 主页:https://ts.gluon.ai/index.html

    • Github:https://github.com/awslabs/gluon-ts/

    • NeuralProphet,推荐指数:⭐⭐⭐⭐

    • 主页:https://neuralprophet.com/

    • Github:https://github.com/ourownstory/neural_prophet

    • arch

    • 介绍:Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python.

    • 主页:https://arch.readthedocs.io/en/latest/

    • Github:https://github.com/bashtage/arch

    • AtsPy

    • 介绍:Automated Time Series Models in Python

    • Github:https://github.com/firmai/atspy

    • banpei

    • 介绍:Anomaly detection library based on singular spectrum transformation

    • Github:https://github.com/tsurubee/banpei

    • cesium

    • 介绍:end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions.

    • 主页:https://cesium-ml.org/

    • Github:https://github.com/cesium-ml/cesium

    • pyfbad

    • Github:https://github.com/Teknasyon-Teknoloji/pyfbad

    更多的模型介绍可以查阅论文[arxiv 2021]A systematic review of Python packages for time series analysis.

    Part3 相关模型

    Time Series Forecasting

    ModelUnivariateMultivariateProbabilisticMultiple-series training
    ARIMA
    VARIMA
    AutoARIMA
    ExponentialSmoothing
    Theta and FourTheta
    Prophet
    FFT (Fast Fourier Transform)
    RegressionModel (incl RandomForest, LinearRegressionModel and LightGBMModel)
    RNNModel (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version
    BlockRNNModel (incl. LSTM and GRU)
    NBEATSModel
    TCNModel
    TransformerModel
    TFTModel (Temporal Fusion Transformer)
    Naive Baselines

    Time Series Classification

    • LSTM FCN,LSTM Fully Convolutional Networks for Time Series Classification

    Anomaly Detection

    • [AAAI 2022] Towards a Rigorous Evaluation of Time-series Anomaly Detection

    Time Series Representation

    • [AAAI 2022] TS2Vec: Towards Universal Representation of Time Series

    Data Augmentation

    • [IJCAI 2021] Time Series Data Augmentation for Deep Learning: A Survey

    • [arxiv 2020] An empirical survey of data augmentation for time series classification with neural networks

    Part4 时序数据集

    • UCR Time Series Classification Archive

    • UEA & UCR Time Series Classification Repository

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  • 原文地址:https://blog.csdn.net/qq_34160248/article/details/126554403