zzc's starred repositories
DiffusionModels
Several Diffusion models, mainly for time series forecasting are implemented
DDM_Timeseries_Forecast
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series to benchmark datasets from different domains
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.
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
DeepLearningForTSF
深度学习以进行时间序列预测
ml-mastery-zh
:book: [译] MachineLearningMastery 博客文章
final-year-project
Load Forecasting using LSTM.
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.
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
LSTM-BP-Load-Forecasing
Load forecasting using LSTM and BP.使用LSTM、BP神经网络实现负荷预测
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.
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.
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.
adversarial-attacks-on-load-forecasting-model
Studied the impact of adversarial attacks on RNN Based load forecasting model.
Load_forecasting
Renewable Energy Load Forecasting using LSTM-RNN
LoadElectricity_Forecasting_CNN-BiLSTM-Attention
Performed comparative analysis of BiLSTM, CNN-BiLSTM and CNN-BiLSTM with attention models for forecasting cases.
STLF-BiLSTM-CNNBiLSTM
Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
Electric-Load-Forecasting
基于LSTM的电力负荷预测
LSTM-Load-Forecasting
Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep forecasting and Multivariate-MultiStep forecasting.
transformers_for_time_series_forecasting
Inferencing 'PatchTST' and 'Informer' to harness the power of transformers for multivariate 'long sequence time-series forecasting' (LSTF).
diffusion-transformer
Implementation of Diffusion Transformer Model in Pytorch
load-point-forecast
load point forecast
simple_pbft_blockchain
a simple blockchain with pbft