Problem: time series forecasting

Challenge: forecasting for non-stationary signals and multiple future steps prediction

?? how to deal with non-stationary datasets??

Introduction

one-step prediction problem VS multi-step prediction;

multi-step forecasting requires to accurately describe time series evolution.

limitation of the euclidean loss(MSE): in non-stationary context;

PP: Shape and time distortion loss for training deep time series forecasting models的更多相关文章

  1. Training (deep) Neural Networks Part: 1

    Training (deep) Neural Networks Part: 1 Nowadays training deep learning models have become extremely ...

  2. Training Deep Neural Networks

    http://handong1587.github.io/deep_learning/2015/10/09/training-dnn.html  //转载于 Training Deep Neural ...

  3. PP: Multi-Horizon Time Series Forecasting with Temporal Attention Learning

    Problem: multi-horizon probabilistic forecasting tasks; Propose an end-to-end framework for multi-ho ...

  4. a Javascript library for training Deep Learning models

    w强化算法和数学,来迎接机器学习.神经网络. http://cs.stanford.edu/people/karpathy/convnetjs/ ConvNetJS is a Javascript l ...

  5. PP: Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting

    Problem: high-dimensional time series forecasting ?? what is "high-dimensional" time serie ...

  6. [Xavier] Understanding the difficulty of training deep feedforward neural networks

    目录 概 主要内容 Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural netwo ...

  7. 论文翻译：BinaryConnect: Training Deep Neural Networks with binary weights during propagations

    目录 摘要 1.引言 2.BinaryConnect 2.1 +1 or -1 2.2确定性与随机性二值化 2.3 Propagations vs updates 2.4 Clipping 2.5 A ...

  8. 论文翻译：BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or −1

    目录 摘要 引言 1.BinaryNet 符号函数 梯度计算和累积 通过离散化传播梯度 一些有用的成分 算法1 使用BinaryNet训练DNN 算法2 批量标准化转换(Ioffe和Szegedy,2 ...

  9. Xavier——Understanding the difficulty of training deep feedforward neural networks

    1. 摘要 本文尝试解释为什么在深度的神经网络中随机初始化会让梯度下降表现很差,并且在此基础上来帮助设计更好的算法. 作者发现 sigmoid 函数不适合深度网络,在这种情况下,随机初始化参数会让较深 ...

随机推荐

  1. P1613 跑路【倍增】【最短路】

    题目描述 小A的工作不仅繁琐,更有苛刻的规定,要求小A每天早上在6:00之前到达公司,否则这个月工资清零.可是小A偏偏又有赖床的坏毛病.于是为了保住自己的工资,小A买了一个十分牛B的空间跑路器,每秒钟 ...

  2. clr via c# delegate

    1,委托列子 internal delegate void Feedback(int value); class DelegateRef { public static void StaticDele ...

  3. 9.16java总结

    枚举   EnunTest.java 运行结果 falsefalsetrueSMALLMEDIUMLARGE 枚举类型可以直接用==来判断是否相等,即代表数据串,又有数的属性.是引用类型. 浮点数计算 ...

  4. css权值和优先级+命名规范

    选择器权值: 标签选择器:1 类选择器和伪类选择器:10 ID选择器:100 通配符选择器:0 行内样式:1000 !important 在一定条件下,优先级最高 常用的css样式命名 页面结构页头: ...

  5. 数据结构与算法 C++ 视频教程（4 套）百度网盘

    为了大二有实力参加算法比赛,大一暑假我选择了留校,提前学习了一下数据结构和算法,这是我找的一些视频资源,分享给大家! 分别是 慕课网 玩转算法与数据结构.慕课网 玩转算法面试.慕课网 玩转数据结构 从 ...

  6. clientHeight offsetTop scrollTop

  7. composer封装dd函数

    1.安装composer,本人用的phpstudy,打开php扩展php_openssl 2.切换到www目录下 cmd 命令 composer 第一个就是我们要找的 3.运行composer req ...

  8. 容错保护机制：Spring Cloud Hystrix

    最近在学习Spring Cloud的知识,现将容错保护机制Spring Cloud Hystrix 的相关知识笔记整理如下.[采用 oneNote格式排版]

  9. private、public、this关键字

    private关键字 概念:私有的,一种权限修饰符,用来修饰类的成员 特点:被修饰的成员只能在本类中访问 用法: - 1. private 数据类型 变量名: - 2. private 返回值类型 方 ...

  10. 聊聊智能指针 auto_ptr、shared_ptr、weak_ptr和unique_ptr

    本文为转载:https://www.cnblogs.com/zeppelin5/p/10083597.html,对作者有些地方做了修正. 手写代码是理解C++的最好办法,以几个例子说明C++四个智能指 ...