Alternatives and similar repositories for DeepLearningForTSF

Users that are interested in DeepLearningForTSF are comparing it to the libraries listed below

• yangwohenmai / TimeSeriesForecasting
  基于统计学的时间序列预测(AR,ARM).
  ☆281Updated 4 years ago
• Tuniverj / Pytorch-lstm-forecast
  基于pytorch搭建多特征LSTM时间序列预测
  ☆171Updated 2 years ago
• HiddenSharp / LSTM-SVM-RF-time-series
  Regression prediction of time series data using LSTM, SVM and random forest. 使用LSTM、SVM、随机森林对时间序列数据进行回归预测，注释拉满。
  ☆191Updated 5 years ago
• wqk666999 / CNN-LSTM-Attention
  使用卷积神经网络-长短期记忆网络（bi-LSTM）-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to…
  ☆288Updated last year
• PatientEz / CNN-BiLSTM-Attention-Time-Series-Prediction_Keras
  CNN+BiLSTM+Attention Multivariate Time Series Prediction implemented by Keras
  ☆701Updated 5 years ago
• sksujan58 / Multivariate-time-series-forecasting-using-LSTM
  ☆254Updated last year
• ki-ljl / LSTM-Load-Forecasting
  Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep f…
  ☆246Updated 3 years ago
• eai2x / TimeSeriesMoonlightBox
  time series analysis models source code
  ☆244Updated 3 years ago
• HugoWang232608 / PM2.5Prediction
  基于LSTM的多变量时间序列预测
  ☆21Updated last year
• taogeanton3 / Informer-1
  PyTorch实现的Informer (Informer:用于长序列时间序列预测
  ☆26Updated 3 years ago
• Heitao5200 / LSTM-for-Time-Series-Forecasting-Pytorch
  使用LSTM、GRU、BPNN进行时间序列预测。Using LSTM\GRU\BPNN for time series forecasting. (Pytorch Edition)
  ☆58Updated 4 years ago
• yangwohenmai / LSTM
  基于LSTM的时间序列预测研究
  ☆3,308Updated 2 years ago
• lwb-xmu / Multivariate-multi-step-time-series-forecasting-via-LSTM
  多元多步时间序列的LSTM模型预测——基于Keras
  ☆82Updated 3 years ago
• koi-boy / CNN-LSTM-Attention
  CNN+LSTM+Attention predict stock
  ☆57Updated 3 years ago
• TonyEinstein / informer_ChineseNotes
  ☆69Updated 2 years ago
• RuifMaxx / Multidimensional-time-series-with-transformer
  transformer/self-attention for Multidimensional time series forecasting 使用transformer架构实现多维时间预测
  ☆234Updated last year
• eai2x / Time-Series-Analysis-Tutorial
  time series analysis tutorial
  ☆909Updated 4 years ago
• yuekong2010 / load-forecasting-algorithms
  使用多种算法（线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）进行电力系统负荷预测/电力预测。通过一个简单的例子。A variety of algorithms (linear regression, random forest, support vecto…
  ☆171Updated 5 years ago
• EthanChenYZ / Time-Series-Forecast
  Codes for time series forecast
  ☆146Updated 4 years ago
• RobinLuoNanjing / air_pollutants_prediction_lstm
  This is a project for predicting air pollutants in London by time series model, including lstm, bilstm, Convlstm, attention lstm, lightGB…
  ☆144Updated 4 years ago
• xingxiaoxuan / ssa-lstm
  ☆19Updated 4 years ago
• FateMurphy / CEEMDAN_LSTM
  CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.
  ☆268Updated 5 months ago
• loveseola / time-series-forecasting
  本项目为时间序列预测项目，主要重点在于对预测项目整体流程的梳理总结，不同框架下如何进行简单数据处理和模型搭建。因此项目中搭建的主要为一些常用模型（后续会不断修改完善）。模型包含了prophet模型、keras库的bp神经网络和lstm网络模型、pytorch …
  ☆24Updated 2 years ago
• pengxiang1998 / DeepLearning
  CNN+LSTM+Attention实现时间序列预测
  ☆57Updated last year
• HAlicia / LSTM-ANN-Time-Series-Prediction
  使用LSTM、ANN网络进行时间序列的多步预测。一般情况下机器学习算法在进行时间序列预测时采取一步预测的方法。该段代码将其拓展到多步预测的情形。主要改进在于数据的构建。LSTM and ANN are used to predict the time series. In …
  ☆16Updated 4 years ago
• Rssevenyu / pytorch-time_series_data-prediction-with-gru-and-lstm
  (pytorch)time_series_data-prediction-with-gru-and-lstm
  ☆47Updated 3 years ago
• litaolemo / Research
  使用ARIMA,Transformer,LSTM 对心跳时间序列数据进行预测
  ☆18Updated last year
• momodagithub / regression-prediction-algorithms
  使用支持向量机、弹性网络、随机森林、LSTM、SARIMA等多种算法进行时间序列的回归预测，除此以外还采取了多种组合方法对以上算法输出的结果进行组合预测。Support vector machine, elastic network, random forest, LSTM…
  ☆46Updated 5 years ago
• sanshuishou / -VMD-Attention-LSTM-
  基于VMD-Attention-LSTM的时间序列预测模型（代码仅使用了一个较小数据集的训练及预测，内含使用使用逻辑，适合初学者观看，模型结构是可行的，有能力的请尝试使用更大的数据集训练）
  ☆63Updated 2 years ago
• mo2ye / SARIMA-LSTM
  建立SARIMA-LSTM混合模型预测时间序列问题。以PM2.5值为例，使用UCI公开的自2013年1月17日至2015年12月31日五大城市PM2.5小时检测数据，将数据按时间段划分，使用SARIMA过滤其线性趋势，再对过滤后的残差使用LSTM进行预测，最后对预测结果进行…
  ☆82Updated 6 years ago