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所在平台: Udemy |
课程主页: https://www.udemy.com/course/forecasting-stock-market-with-arima-model-time-series/
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课程名称：使用ARIMA模型和时间序列预测股市 课程概述：欢迎参加使用ARIMA模型和时间序列进行股市预测的课程。本课程是一个综合性的项目导向课程，您将逐步学习如何对股市数据进行复杂的分析和可视化，重点在于使用ARIMA模型预测未来股价。课程将使用Python编程语言，并结合Pandas、Numpy和Matplotlib等多个库进行数据建模、复杂计算和数据可视化。 在课程的介绍环节，您将学习股市预测的基本原理，包括影响预测准确性的因素以及将使用的模型。接着，您将深入学习股市预测背后的基础数学，包括如何手动计算移动平均。此外，您还将学习ARIMA模型的数学原理，并通过一个综合案例研究手动计算ARIMA模型的应用。随后，您将了解多种内部和外部因素，诸如市场情绪、收益报告和利率，这些因素可能会影响股市。 在掌握了股市预测所需的基本知识后，课程将进入项目实践部分。首先，您将学习如何设置Google Colab，这是我们将使用的集成开发环境。然后，您将学习如何从Kaggle寻找和下载股市数据集。一旦所有准备工作完成，您将进入课程的主要部分，即项目部分，在此我们将大多数时间用于进行数据集实验。 最后，您还将学习几种评估预测模型性能的指标，例如平均绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE），并了解如何在简单数据集上应用这些指标。在课程开始之前，您可能会问自己一些问题：为什么我们要学习预测股市？我们如何判断预测的准确性？投资者自上百年前以来便开始投资股市，而随着技术和大数据的发展，将大数据技术与股市投资相结合变得尤为重要，能够帮助投资者识别历时数据中的模式以预测未来。然而，值得注意的是，预测股市并无百分之百的准确率，尽管历史数据中出现的重复趋势确实可以提高未来发生同样模式的可能性。 通过本课程，您将能够学习到以下内容： - 掌握股市预测的基本原理和相关因素 - 学会如何计算移动平均 - 将ARIMA模型应用于简单数据集并进行基础预测 - 识别可能影响股市的各种因素 - 在Kaggle寻找和下载数据集 - 向Google Colab上传数据 - 清理数据集，包括去除缺失值和重复值 - 分析和可视化每年平均最高和最低股价 - 计算100日移动平均 - 进行自相关和偏自相关分析 - 使用ARIMA模型进行预测和残差分析 - 进行预测模型评估并计算MAE、MSE和RMSE 本课程将为您提供在股市预测领域的实用技术和知识基础。
Welcome to Forecasting Stock Market with ARIMA Model & Time Series course. This is a comprehensive project based course where you will be guided step by step on how to perform complex analysis and visualisation on stock market data, in addition, the course will be concentrating mainly on forecasting future stock prices using ARIMA model and implementing time series. For the programming language, we are going to use Python alongside several libraries like Pandas for performing data modelling, Numpy for performing complex calculations, and Matplotlib for visualising the data. In the introduction session, you will learn the basic fundamentals of stock market forecasting, such as getting to know factors that affect forecasting accuracy and models that will be used in forecasting. Then, continuing by learning the basic mathematics behind forecasting stock market, you will learn step by step on how to calculate moving averages manually. Not only that, you are also going to learn the mathematics behind the ARIMA model, there will be one comprehensive case study to teach you how to do manual calculation using the ARIMA model. Afterward, you will also learn several internal and external factors that could potentially impact the stock market, for example market sentiment, earning reports, and interest rates. Once you've learnt all necessary knowledge about stock market forecast, we will begin the project, firstly, you will learn how to set up Google Colab since that is the IDE that we are going to use, Then, you will also learn how to find and download stock market datasets from Kaggle. Once everything is all set, you will enter the main section of the course which is the project section where we are going to spend most of our time here, conducting experiments with the dataset. Lastly, at the end of the course, you also learn several metrics for evaluating forecasting model performance, such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error, in addition, you will also learn how to implement those metrics on a simple dataset.First of all, before getting into the course, we need to ask ourselves these questions: why should we learn to forecast the stock market? How are we able to know if the forecast is accurate? Well, in my opinion, there are many answers to those questions. Firstly, people have been investing in the stock market since a hundred years ago, therefore, this type of investment has been around for a long time. As the advancement of technology and big data nowadays, people started to realize that integrating big data technology into stock market investing is going to be extremely beneficial as it allows investors to identify patterns from the historical data to make a prediction about the future. Then, the next question might potentially be, how accurate is the forecast going to be? Well, there is no such thing as 100% accuracy. When it comes to forecasting the stock market, we use the data from the past to make a data driven investment decision. Nonetheless, no matter how convinced we are with a pattern from the historical data, there is still no 100% guarantee that the same exact pattern will repeat itself in the future. However, when you spot a repetitive trend or pattern in the data, it basically indicates there is a higher chance that the pattern will happen in the future and that is what the forecasting model is actually based on.Below are things that you can expect to learn from this course:Learn basic fundamentals of stock market forecasting, such as getting to know factors that affect the forecasting accuracy and several forecasting models that will be usedLearn how to calculate moving averageLearn how to apply ARIMA (Autoregressive Integrated Moving Average) model into simple dataset and do the basic forecastingLearn several internal and external factors that can potentially impact stock marketLearn how to find and download datasets from KaggleLearn how to upload data to Goolge Colab StudioLearn how to clean the dataset by removing missing values and duplicate valuesAnalysing & visualising average highest & average lowest stock price per yearAnalysing & visualising average volumeFinding correlation between volume & price changesCalculating 100 days moving averageAnalysing & visualising volatilityLearn how to analyse autocorrelation function & partial autocorrelation functionLearn how to perform forecasting using ARIMA modelLearn how to perform residual analysisLearn how to do forecasting model evaluation by calculating MAE (Mean Absolute Error), MSE (Mean Squared Error), and RMSE (Root Mean Squared Error)
