时间序列预测(最终使用python) [英] Time series forecasting (eventually with python)

问题描述

• 存在哪些用于时间序列预测/回归的算法?
  • 使用神经网络怎么样? (有关此主题的最佳文档?)
  • 是否有可以帮助您的python库/代码段?
  • What algorithms exist for time series forecasting/regression ?
    • What about using neural networks ? (best docs about this topic ?)
    • Are there python libraries/code snippets that can help ?

    推荐答案

    时间序列回归的经典方法是:

    The classical approaches to time series regression are:

    • 自动回归模型(关于它们的完整文献)

    • auto-regressive models (there are whole literatures about them)

    高斯过程

    傅里叶分解或类似方法以提取信号的周期性分量(即数据中的隐藏振荡)

    Fourier decomposition or similar to extract the periodic components of the signal (i.e., hidden oscillations in the data)

    我知道的其他较不常见的方法是

    Other less common approaches that I know about are

    • 慢特征分析，一种提取时间序列驱动力的算法，例如，混沌信号后面的参数

    • Slow Feature Analysis, an algorithm that extract the driving forces of a time series, e.g., the parameters behind a chaotic signal

    神经网络(NN)方法，是使用递归NN(即用于处理时间信号的)或经典前馈NN来接收过去数据的输入部分，并尝试预测未来的某个点;后者的优势在于，已知循环神经网络在考虑遥远的过去时会遇到问题

    Neural Network (NN) approaches, either using recurrent NNs (i.e., built to process time signals) or classical feed-forward NNs that receive as input part of the past data and try to predict a point in the future; the advantage of the latter is that recurrent NNs are known to have a problem with taking into account the distant past

    在我看来，对于财务数据分析，重要的是不仅要获得时间序列的最佳推测，而且要获得可靠的置信区间，因为由此产生的投资策略可能会大不相同.概率方法(例如高斯过程)会免费"为您提供这种方法，因为它们会返回可能的未来值的概率分布.使用经典的统计方法，您将不得不依靠引导技术.

    In my opinion for financial data analysis it is important to obtain not only a best-guess extrapolation of the time series, but also a reliable confidence interval, as the resulting investment strategy could be very different depending on that. Probabilistic methods, like Gaussian Processes, give you that "for free", as they return a probability distribution over possible future values. With classical statistical methods you'll have to rely on bootstrapping techniques.

    有许多提供统计和机器学习工具的Python库，以下是我最熟悉的库:

    There are many Python libraries that offer statistical and Machine Learning tools, here are the ones I'm most familiar with:

    • NumPy 和 RPy
    • statsmodel 包含经典的统计模型技术，包括自回归模型；它可与 Pandas (流行的数据分析包
    )配合使用
    • scikits.learn ， MLPy ，橙色是机器学习算法的集合
    • PyMC 一个实现贝叶斯统计模型和拟合算法(包括马尔可夫链蒙特卡洛)的python模块
    • PyBrain 包含(除其他事项外)前馈和循环神经网络的实现
    • 在高斯过程站点中，有一系列GP软件，包括两个Python实现
    • mloss 是开源机器学习软件的目录
    • NumPy and SciPy are a must for scientific programming in Python
    • There is a Python interface to R, called RPy
    • statsmodel contains classical statistical model techniques, including autoregressive models; it works well with Pandas, a popular data analysis package
    • scikits.learn, MDP, MLPy, Orange are collections of machine learning algorithms
    • PyMC A python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo.
    • PyBrain contains (among other things) implementations of feed-forward and recurrent neural networks
    • at the Gaussian Process site there is a list of GP software, including two Python implementations
    • mloss is a directory of open source machine learning software

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