Forecasting Stock Market with ARIMA Model & Time Series

Partner: Udemy
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Description: 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 tho
Category: Finance & Accounting > Financial Modeling & Analysis > Financial Markets
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Price: 19.99
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Source: Impact
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