时序资料汇总:模型和常见库对比

发布于:2023-01-20 ⋅ 阅读:(411) ⋅ 点赞:(0)

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

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

Forecasting Classsification Anomaly Detection Segmentation TSFeature
Prophet
Kats
GluonTS
NeuralProphet
arch
AtsPy
banpei
cesium
darts
PaddleTS

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

Model Univariate Multivariate Probabilistic Multiple-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

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

Part4 时序数据集


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