# LSTM 时间序列分析预测 目录
使用LSTM神经网络进行时间序列数据预测分析。
基于Tensorflow框架、Kerase接口开发网络模型。
包含数据清洗,数据特征提取,数据建模,数据预测。
# LSTM 时间序列分析预测 目录
# 一、LSTM系列
### 1.LSTM单变量(shampoo-sales)
+ ***1_1.LSTM单变量1***
```
1.香皂销售预测(很多饮料瓶图标)
```
+ ***1_2.LSTM单变量2***
```
1.观测值缩放
2.时间序列转换成稳定数据
3.时间序列转监督学习数据
```
+ ***1_3.LSTM单变量3***
```
1.LSTM模型开发
```
+ ***1_4.LSTM单变量4***
```
1.完整的LSTM案例
```
+ ***1_5.LSTM单变量5***
```
1.更健壮的LSTM案例
```
### 2.LSTM多变量(air_pollution)
+ ***1_1.LSTM多变量1***
```
1.数据输出
2.预处理
```
+ ***1_2.LSTM多变量2***
```
1.LSTM数据预处理
```
+ ***1_3.LSTM多变量3***
```
1.定义&训练模型
2.数据预处理
```
### 3.Multi-Step LSTM预测(shampoo-sales)
+ ***1_1.Multi-Step LSTM预测1***
```
1.静态模型预测
```
+ ***1_2.Multi-Step LSTM预测2***
```
1.多步预测的LSTM网络
```
# 二、LSTM_Fly(airline-passengers)
```
1.LSTM回归网络(1→1)
2.移动窗口型回归(3→1)
3.时间步长型回归(3→1)
4.批次之间具有记忆的LSTM
5.批次之间具有堆叠的LSTM
```
# 长短周期记忆网络(LSTM)
## 一、LSTM的特性
### 1.使用编码器-解码器LSTM来回显随机整数序列
```
01.准备回声序列数据
02.预测回声序列
03.编码器 - 解码器模型
04.代码2回声序列的一个简易版,可观测数据
```
### 2.输入输出对和TimeDistributed
```
01.一对一LSTM
02.多对一LSTM(没有TimeDistributed)
03.多对多LSTM(具有TimeDistributed)
```
### 3.有状态网络的输入输出对预测
```
01.输入-输出对
02.重塑数据
03.有状态的LSTM网络的完整示例
```
## 二、Keras中长短期记忆模型的5步生命周期
```
01.Keras中长短期记忆模型的5步操作
02.Keras中长短期记忆模型的5步操作代码分析
```
## 三、LSTM的数据准备
### 1.处理序列预测中的缺失时间步长
```
01.创建一个序列的演示
02.对缺失值进行学习
03.忽略缺失的学习
04.删除缺失的序列数据
05.替换缺失的序列数据
```
### 2.归一化标准化长短期内存网络的数据
```
01.标准化
02.归一化
```
### 3.使用差异变换消除趋势和季节性
```
01.差分用于消除季节性
02.差分用于消除趋势
```
### 4.在编写one-hot编码序列数据
```
01.one-hotenoder与Keras
02.one-hotenoder与scikit学习
03.手动one-hotenoder
```
### 5.重塑Keras中长短期内存网络的输入数据
```
01.单输入样本的LSTM示例
02.具有多个输入功能的LSTM示例
03.如何为长短期记忆网络准备单变量时间序列数据
```
## 四、使用LSTM建模
### 1.堆叠的长短期记忆网络
```
01.在Keras中实现堆叠LSTM 2D输出
02.在Keras中实现堆叠LSTM 3D输出
```
### 2.如何调用Keras中的长短期记忆模型
```
01.保存模型并加载
```
### 3.如何诊断LSTM模型的过度拟合和欠拟合
```
01.训练周期不足
02.合格的例子
03.过度拟合的例子
04.多次拟合评估
```
## 五、LSTM实例
### 1.空气质量(多变量预测)
```
01.数据准备
02.数据画图展示
03.将数据转换成监督学习数据
04.一天预测一天
05.三天预测一天
```
### 2.洗发水销量(单步预测)
```
01.数据集图示
02.构造简单的滞后模型
03.构造监督型数据结构
04.数据差分法
05.数据缩放法
06.LSTM模型实例
07.LSTM模型性能测评
08.代入股票数据测试
09.不做差分的股票数据预测
10.抽取数据做验证集画损失图
```
### 3.洗发水销量(多步预测)
```
01.数据集图示
02.监督学习型数据准备
03.静态假数据预测效果
04.训练神经网络预测
```
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