搜索资源列表
ARtesting
- 在MATLAB平台,利用AR模型对时间序列进行预测,采用ar()函数编程-In the MATLAB platform, the use of AR time series model to predict, using ar () function programming
FunctionChaosPredict
- 利用一阶局域加权法进行混沌时间序列的预测。-Using a weighted-rank local-region method of forecasting chaotic time series.
ElmanRnn
- Elman递归神经网络对时间序列的预测代码,做的效果还行,仅供参考-Elman recurrent neural network for time series prediction code, do the results were OK for reference purposes only
wnn_forcast
- 用小波神经网络变换对时间序列信号进行预测,并做了测试,效果很好,请参考-Transform using wavelet neural network to predict the time series signal, and do a test with good results, please refer to
gl
- 基于关联规则的股票时间序列趋势预测研究 硕士毕业论文-Based on Association Rules Stock Market Time Series Prediction Research Master Thesis
Based_on_the_maximum_Lyapunov_exponent_of_the_chao
- 基于最大李亚普诺夫指数的改进混沌时间序列预测的方法-Based on the maximum Lyapunov exponent of the improved chaotic time series prediction methods
Model_ARIMA1
- 季节性移动自回归模型 可以进行时间序列的预测 尤其是季节性数据-S-Arima seaonal Arima model in matlab
TSregressionforecasting
- 时间序列工具箱,包含大量回归与预测函数以及实例-Time Series Regression and Forecasting
matlabARMA
- 在matlab下时间序列分析ARMA模型的建立和预测程序ARMA-Under the matlab time series analysis and forecasting ARMA model procedures for ARMA
simulation
- 时间序列的预测及中间状态,包括误差检测以及其和中间状态的关系-representation of status
4Chaos_Prediction
- 关于混沌时间序列的预测工具箱,可以方便各行各业人员预测-chaotic predictability
suijixingshijianxulieyuce
- 随机型时间序列预测法PPT讲义,讲解比较详细,需要的朋友可以-Then type time series forecasting method PPT handouts to explain in more detail, you need friends can see
spark-timeSeries
- 采用ARIMA模型(自回归积分滑动平均模型)+三次指数平滑法(Holt-Winters),用scala语言实现的在spark平台运行的分布式时间序列预测算法(Using the ARIMA model (autoregressive integral moving average model) + Holt-Winters (Holt-Winters), using scala language to achieve the spark
BP神经网络预测算法MATLAB源程序
- BP神经网络预测算法MATLAB源程序,用于混沌时间序列预测。(BP neural network prediction algorithm MATLAB source code for chaotic time series prediction.)
指数型加权零阶局与预测法
- 加权零阶局域预测法对时间序列数据进行短期预测(Calculating the Poincare cross section of time series)
小波神经网络的时间序列预测-短时交通流量预测
- 本文采用小波神经网络进行交通流量预测,短时交通流量存在随机性和非线性因素,影响预测的准确性。传统预测模型难以反映交通流量变化特点,同时传统神经网络易陷入局部极小值,泛化能力差,交通流量预测精度低。为了提高短时交通流量预测精度,提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析,并进行非线性预测,验证了模型的有效性,进行了对比试验。验证结果表明,小波神经网络提高了短时交通流量预精度,预测
LSTM时间序列预测
- 本代码采用python语言编写的的一个LSTM时间序列来预测销量(This code uses a LSTM time series written in Python language to predict sales)
LSTM程序
- 基于LSTM的时间序列预测-原理-python代码(Prediction of time series based on LSTM - principles -python code)
scmt.m
- 用PSO-BPNN算法对时间序列数据进行拟合并预测未来一段时间数据(Using pso-bpnn algorithm to predict the future time series data)
LSTM股票预测
- Lstm进行时间序列预测,预测股票数据,按日的数据(Prediction of time series by LSTM)