搜索资源列表
LSSVM
- lssvm用于时间序列预测的matlab程序(LSSVM for time series prediction)
elmen网络代码
- ELMAN神经网络模型预测精度高于BP神经网络,可以用于非线性时间序列的预测。(The prediction accuracy of ELMAN neural network model is higher than that of BP neural network, and it can be used to predict nonlinear time series.)
ARIMA
- arima算法实现思路,主要用来进行时间序列预测(The realization of ARIMA algorithm is mainly used for time series prediction)
lstm
- 使用lstm神经网络预测时间序列,同时对参数选择进行优化(Time series prediction using LSTM neural network, the selection of the parameters are optimized)
yesllmg6
- 时间序列预测法是一种定量分析方法,它是在时间序列变量分析的基础上,运用一定的数学方法建立预测模型,使时间趋势向外延伸,从而()
loess-master
- 局部加权平均算法,用于时间序列预测,大家看着给点好处就好(Local weighted average algorithm for time series prediction.)
lssvm_prediction
- LSSVM时间序列预测,基于matlab lssvm工具箱!推荐!!!!!(LSSVM time series prediction is based on MATLAB LSSVM toolbox. Recommend!!!!!)
sjyc
- 时间预测的应用需要garchset.m,在2012版本以上的可能无法实现(The application of time prediction needs garchset.m)
LSTM_main
- LSTM(Long Short-Term Memory)是长短期记忆网络,是一种时间递归神经网络,适合于处理和预测时间序列中间隔和延迟相对较长的重要事件。(LSTM (Long Short-Term Memory) is a long and short term memory network. It is a kind of time recurrent neural network, which is suitable for dealing with and predicting impo
c-c算法计算时间tau
- 在混沌时间序列方法进行风电功率预测用C-C算法tau时间(C-C algorithm tau time for wind power prediction using chaotic time series method)
code
- 数据处理预测;时间序列;前期数据处理;后期GRU预测(Data processing prediction)
LSTM2
- LSTM可用的matlab程序,可用到时间序列预测(Matlab code for predicting via LSTM algorithm)
ARMA-master
- 程序附带说明,时间序列预测模型ARMA模型,非平稳时间序列预测(Program with instructions, time series prediction model ARMA)
LargestLyapunov_Rosenstein
- 混沌时间序列预测中,重构相空间采用小数据量法求取最大李雅普莫诺夫指数。(In the prediction of chaotic time series, the maximum lyapunov index is obtained by the method of small data quantity in the reconstructed phase space.)
23_time_series_prediction
- 用 Python 机器学习,时间序列模型预测, 循环神经网络(Python Machine Learning, Time Series Model Prediction, recurrent Neural Network)
ARMA
- 利用ARMA时间序列模型 预测短期内风速(Forecasting wind speed with ARMA)
电力负荷组合预测
- 基于svM以及时间序列法的短期电力负荷预测,MATLAB源代码 谢谢
ruan zhu
- 一个用DBN做时间序列预测的实例,内包括了数据(An example of using DBN to predict time series includes data)
Python深度学习实战_原书代码
- 深度学习正在为广泛的行业带来革命性的变化。对于许多应用来说,深度学习通过做出更快和更准确的预测,证明其已经超越人类的预测。本书提供了自上而下和自下而上的方法来展示深度学习对不同领域现实问题的解决方案。这些应用程序包括计算机视觉、自然语言处理、时间序列预测和机器人。(Deep learning is bringing revolutionary changes to a wide range of industries. For many applications, deep learning p
GRU预测模型
- 使用python编程,对时间序列进行预测建模。参数较少,预测比较准确,运行时间快。