zzc-dtt

zzc-dtt's Stars

• Sniper970119/dianping_spider

  大众点评爬虫（全站可爬，解决动态字体加密，非OCR）。持续更新

  Language:Python9621852167

• apachecn/ml-mastery-zh

  :book: [译] MachineLearningMastery 博客文章

  Language:JavaScript701294182

• yangwohenmai/DeepLearningForTSF

  深度学习以进行时间序列预测

  Language:Python60760168

• ki-ljl/LSTM-Load-Forecasting

  Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep forecasting and Multivariate-MultiStep forecasting.

  Language:Python21931048

• thuml/Koopa

  Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803

  Language:Python19842525

• 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 vector machine, BP neural network, GRU, LSTM) are used for power system load forecasting / power forecasting.

  Language:Jupyter Notebook1551038

• benjamingli/Electric-Load-Forecasting

  基于LSTM的电力负荷预测

  Language:Python1423554

• hyajam/jabs

  a blockchain network simulator aimed at researching consensus algorithms for performance and security

  Language:Java816224

• milmor/diffusion-transformer

  Implementation of Diffusion Transformer Model in Pytorch

  Language:Python46227

• JeCase/LoadElectricity_Forecasting_CNN-BiLSTM-Attention

  Performed comparative analysis of BiLSTM, CNN-BiLSTM and CNN-BiLSTM with attention models for forecasting cases.

  Language:Jupyter Notebook36125

• absaw/DDM_Timeseries_Forecast

  Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series to benchmark datasets from different domains

  Language:Jupyter Notebook30303

• Doheon/TimeSeriesForecast-Informer

  Language:Jupyter Notebook30116

• Varat7v2/STLF-BiLSTM-CNNBiLSTM

  Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM

  Language:Jupyter Notebook29205

• Garyou19/LSTM_PyTorch_Electric-Load-Forecasting

  使用PYTorch框架建立的一个简单的LSTM模型来进行电力负荷预测

  Language:Python264

• kushwahavishal646/Load-Forecasting-using-Different-Deep-Learning-Architectures

  this project is to implement different deep learning architectures and evaluate them based on their performance on the hour-ahead electricity price and load prediction task. More specifically, we will evaluate (i) Random Forest, (ii) CNN-Univariate, (iii) CNN-Multivariate, (iv) RNN-LSTM and (v) BiLSTM architectures, using the root mean squared error (RMSE). Furthermore, we will experiment on different task formulations and types of frameworks, alongside the two following dimensions: • We will compare the performance of univariate time series forecasting and multivariate time series forecasting. Univariate time series forecasting is a framework on which the predicted quantity (i.e. electricity price) is the sole feature that is used by the models, whereas the multivariate variant of the task also uses other features which may prove important for the prediction, such as the load of the energy grid, the temperature, etc. • We will compare the performance of using different time-steps (3, 10 and 25 time-lags) as a way of reframing the time-series prediction task into a supervised learning problem, i.e. using the past 3, 10 and 25 values of the features which are fed into our models.

  Language:Jupyter Notebook25117

• Lord-Fec/LSTM-BP-Load-Forecasing

  Load forecasting using LSTM and BP.使用LSTM、BP神经网络实现负荷预测

  161

• neelblabla/transformers_for_time_series_forecasting

  Inferencing 'PatchTST' and 'Informer' to harness the power of transformers for multivariate 'long sequence time-series forecasting' (LSTF).

  Language:Jupyter Notebook14106

• Mark-THU/load-point-forecast

  load point forecast

  Language:Jupyter Notebook13213

• DCHuTJU/goPBFT

  A simple consensus of PBFT

  Language:Go11114

• LiYuxin321/TMDM

  Code for the paper Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting

  Language:Python11222

• ksb1712/Load_forecasting

  Renewable Energy Load Forecasting using LSTM-RNN

  Language:Jupyter Notebook9200

• umesh2908/LSTM-time-series-forecasting-of-AG-load

  Develop a Time series forecasting model using LSTM deep learning model.15 month AMR data of Agriculture load in 30 minutes intervals have used to train the model and made the prediction for the next one month.

  Language:Jupyter Notebook8311

• adrianatienza1996/DiffusionModels

  Several Diffusion models, mainly for time series forecasting are implemented

  Language:Python5220

• kid1999/simple_pbft_blockchain

  a simple blockchain with pbft

  Language:Go4100

• rishmi5h/final-year-project

  Load Forecasting using LSTM.

  Language:Jupyter Notebook40

• BharathGottumukkala/pBFT

  Demo Byzantine fault tolerance

  Language:Python32

• Avinash793/adversarial-attacks-on-load-forecasting-model

  Studied the impact of adversarial attacks on RNN Based load forecasting model.

  Language:Jupyter Notebook2100

• eddies505/Capstone

  Weather forecasting using Vision Transformer and diffusion models

  Language:Jupyter Notebook1

• Makatjane/Combined-forecasting-using-Stacking-ensemble-algorithm

  In this notebook, I developed a combined forecasting model using stacking ensemble algorithm. My base learners are Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The data used to perform this experiment is hourly electricity demand load from ESKOM.

  1

• rishiiiiiiiiiiiiiiiiiiiiiiiii/Time-Series-Modeling-and-Prediction-of-Microsoft-Stock-Prices-Using-ARIMA

  Utilized Microsoft stock data, employing techniques such as seasonal decomposition, stationary testing, and log transformations & Conducted data analysis, trend identification, and seasonality assessment, optimizing the model configuration using auto-ARIMA and achieving accurate fitting over a 6-year period.

  Language:Jupyter Notebook12