Powell 无导数优化求解器。可求解黑箱优化，用于机器学习超参数调节。详情见项目主页 https://www.pdfo.net 。
这是一个使用Three.js制作的深海潜水器运动模拟的3D demo系统，后台的轨迹记录功能使用Django制作，功能有点粗糙，需要大家的力量，我相信它能用来做一些好玩的东西。。
此项目从无到有搭建一个以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务
（知识图谱+目标检测），使得小目标，或者被遮挡目标也能被检测出来
Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets
Statistical Analysis and predicting future TeleCommunication Data by SVR, ARIMA, LSTM, Conv_LSTM
基于ARIMA时间序列的销量预测模型，实际预测准确率达90%以上，内含有测试记录和实际上线效果。
这一篇notebook是关于我写的毕业论文的整个过程的方法和总结，包括了我在写论文时的一些感悟和体验。
Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy
A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats
Seq2Seq、Bert、Transformer、WaveNet 用于时间序列预测。
This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.
