Python在时间序列分析和预测中的应用

商业银行如何预测其贷款组合的预期表现？或者投资经理如何估计股票投资组合的风险？哪些是用于预测房地产的定量方法？如果存在某种时间依赖性，那么你是知道的——答案是:时间序列分析。

这门课程将教会你一些实用的技能，让你能够找到一份定量金融分析师、数据分析师或数据科学家的工作。很快，你将获得基本技能，使你能够执行复杂的时间序列分析，直接应用于实践。我们已经创建了一个时间序列课程，它不仅是永恒的，而且:易于理解，全面实用，有大量的练习和资源，但我们知道这可能还不够。我们采用最突出的工具，并通过Python——目前最流行的编程语言——来实现它们。记住这一点…欢迎学习Python中的时间序列分析！参加在线课程的最大问题是期望什么。我们已经确保为您提供了精通时间序列分析所需的一切。我们从探索基本的时间序列理论开始，帮助你理解随后的建模。Applied Time Series Analysis And Forecasting In Python

在整个课程中，我们将使用大量的Python库，为您提供完整的培训。我们将使用pandas内置的强大的时间序列功能，以及其他基础库，如NumPy、matplotlib、StatsModels、yfinance、ARCH和pmdarima。有了这些工具，我们将掌握最广泛使用的模型:AR(自回归模型)MA(移动平均模型)ARMA(自回归移动平均模型)ARIMA(自回归综合移动平均模型)ARIMAX(带外生变量的自回归综合移动平均模型)。SARIA(季节性自回归移动平均模型)。SARIMA(季节性自回归综合移动平均模型)。SARIMAX(带外生变量的季节自回归综合移动平均模型)ARCH(自回归条件异方差模型)GARCH(广义自回归条件异方差模型)。VARMA(向量自回归移动平均模型)我们知道时间序列是那些总是留下一些疑问的话题之一。直到现在。

这门课正是你一劳永逸理解时间序列所需要的。不仅如此，你还会得到大量的额外材料——笔记本文件、课程笔记、测验问题和许多许多练习——一切都包括在内。这是唯一一门结合最新统计学和深度学习技术进行时间序列分析的课程。首先，本课程涵盖了时间序列的基本概念:平稳性和增广的Dicker-Fuller检验季节性白噪声随机游走自回归移动平均CF和PACF，用AIC (Akaike的信息准则)进行模型选择然后，我们继续并应用更复杂的统计模型进行时间序列预测:ARIMA(自回归综合移动平均模型)SARIMA(季节自回归综合移动平均模型)SARIMAX(带有外生变量的季节自回归综合移动平均模型)我们还涵盖了多个时间序列预测:VAR(向量自回归)VARMA(向量自回归移动 我们继续学习深度学习部分，在这里我们将使用Tensorflow应用不同的深度学习技术进行时间序列分析:简单线性模型(1层神经网络)DNN(深度神经网络)CNN(卷积神经网络)LSTM(长短期记忆)CNN + LSTM模型(残差网络)自回归LSTMThroughout在整个课程中，您将使用Python完成5个以上的端到端项目，所有源代码都可供您使用。

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言:英语|大小:4.67 GB 含课程文件 |时长:8小时28分钟

Python中的时间序列分析:理论，建模:AR到SARIMAX，向量模型，GARCH，自动ARIMA，预测

你会学到什么
理解在比较不同的时间序列时对数据进行标准化的需要。
遇到特殊类型的时间序列，如白噪声和随机漫步。
了解如何通过移动平均线计算“意外冲击”。
开始用Python编码，并学习如何使用它进行统计分析。

要求
希望获得时间序列经验的初级数据科学家
对量化金融感兴趣的人。
有抱负的数据科学家。
想专攻金融的程序员。

概观
第1部分:PYTHON——面向初学者的PYTHON基础介绍

第1讲Python -数据结构(列表、元组、字典)和字符串操作

第2讲Python-Lambda，递归，函数的实现。

第三讲Python——理解库、探索性数据分析、描述性分析

第2节:用于数据分析的商业统计基础

第4讲统计学导论和集中趋势的度量

第五讲中心极限定理- CLT

第6讲分布和相关性

第7讲PDF & CDF和假设检验

第三节:时间序列分析-初学者时间序列基础介绍

第八讲时间序列——时间序列数据的特征和分解

第9讲时间序列-概率、统计和预测模型的最佳实践

第十讲时间序列-对医学数据时间序列分析的实际理解

第四节:顶点工程:UK _ Road _ Accident _ time series _ Forecasting _ EDA

第11讲英国_道路_事故_时间序列_预测_EDA

第12讲根据萨里玛、FbP、LSTM的伤亡人数预测英国事故率

有抱负的数据科学家。、需要分析时间序列的专业数据科学家、对时间序列好奇的深度学习初学者

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting

What you’ll learn
Comprehend the need to normalize data when comparing different time series.
Encounter special types of time series like White Noise and Random Walks.
Learn about accounting for “unexpected shocks” via moving averages.
Start coding in Python and learn how to use it for statistical analysis.

Requirements
Beginner data scientists looking to gain experience with time series
People interested in quantitative finance.
Aspiring data scientists.
Programmers who want to specialize in finance.

Description
How does a commercial bank forecast the expected performance of their loan portfolio?Or how does an investment manager estimate a stock portfolio’s risk?Which are the quantitative methods used to predict real-estate properties?If there is some time dependency, then you know it – the answer is: time series analysis.This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:· Easy to understand· Comprehensive· Practical· To the point· Packed with plenty of exercises and resourcesBut we know that may not be enough.We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…Welcome to Time Series Analysis in Python!The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis.We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.With these tools we will master the most widely used models out there:· AR (autoregressive model)· MA (moving-average model)· ARMA (autoregressive-moving-average model)· ARIMA (autoregressive integrated moving average model)· ARIMAX (autoregressive integrated moving average model with exogenous variables). SARIA (seasonal autoregressive moving average model). SARIMA (seasonal autoregressive integrated moving average model). SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)· ARCH (autoregressive conditional heteroscedasticity model)· GARCH (generalized autoregressive conditional heteroscedasticity model). VARMA (vector autoregressive moving average model)We know that time series is one of those topics that always leaves some doubts.Until now.This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:stationarity and augmented Dicker-Fuller testseasonalitywhite noiserandom walkautoregressionmoving averageACF and PACF,Model selection with AIC (Akaike’s Information Criterion)Then, we move on and apply more complex statistical models for time series forecasting:ARIMA (Autoregressive Integrated Moving Average model)SARIMA (Seasonal Autoregressive Integrated Moving Average model)SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)We also cover multiple time series forecasting with:VAR (Vector Autoregression)VARMA (Vector Autoregressive Moving Average model)VARMAX (Vector Autoregressive Moving Average model with exogenous variable)Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:Simple linear model (1 layer neural network)DNN (Deep Neural Network)CNN (Convolutional Neural Network)LSTM (Long Short-Term Memory)CNN + LSTM modelsResNet (Residual Networks)Autoregressive LSTMThroughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.

Overview
Section 1: PYTHON – Introduction to Basics of Python for Beginners

Lecture 1 Python – Data Structures (Lists, Tuple, Dictionary) and String Manipulations

Lecture 2 Python – Implementation Of Lambda, Recursion, Functions.

Lecture 3 Python – Understand Of Libraries,Exploratory Data Analysis,Descriptive Analysis

Section 2: Foundations of Business Statistics for Data Analysis

Lecture 4 Introduction to statistics and Measures of central tendencies

Lecture 5 Central Limit Theorem – CLT

Lecture 6 Distributions and Correlations

Lecture 7 PDF & CDF and Hypothesis Testing

Section 3: TIME SERIES ANALYSIS – Introduction to Basics of Time Series for Beginners

Lecture 8 TIME SERIES – Characteristics and Decomposition of Time Series Data

Lecture 9 TIME SERIES – Best Practices of Probability, Statistics and Forecasting Models

Lecture 10 TIME SERIES – Practical Understanding of Time Series Analysis with Medical Data

Section 4: Capstone Project : UK_Road_Accident_Timeseries_Forecasting_EDA

Lecture 11 UK_Road_Accident_Timeseries_Forecasting_EDA

Lecture 12 Forecast UK Accident rates based on Number of Casualties on SARIMA,FbP,LSTM’s

Aspiring data scientists.,Professional data scientists who need to analyze time series,Deep learning beginners curious about times series