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# Stock Market Forecasting in R – Polynomial Order 2 model using EuStockMarket dataset

Stock market forecasting is the process of using historical data and statistical models to predict future movements of stock prices. One of the methods for stock market forecasting is a polynomial model of order 2, which is a type of mathematical model that assumes that the relationship between the input and output variables is not linear and can be described by a polynomial function of order 2.

One of the datasets that can be used for a polynomial model of order 2 is the EuStockMarket dataset which has historical data of stock prices from the European stock market. The goal of using a polynomial model of order 2 on the EuStockMarket dataset is to predict future stock prices based on the historical data.

The process of building a polynomial model of order 2 typically involves the following steps:

**Collecting and cleaning the data:** This includes acquiring the EuStockMarket dataset and preparing it for analysis.

**Decomposition of time series:** This step is optional but it is useful to understand the trend, seasonality and residual components of the time series.

**Choosing an appropriate polynomial model of order 2:** This could be a simple polynomial regression of order 2 or multiple polynomial regression of order 2.

**Training the model:** This includes estimating the parameters of the model, such as the coefficients, using the historical data.

**Forecasting:** This includes using the trained model to predict future stock prices.

**Evaluation:** This includes evaluating the model’s performance on a separate test dataset and comparing it to other models or to a baseline.

It is important to note that stock market forecasting is a complex task and there are many factors that can affect the accuracy of the forecasts. Additionally, Polynomial models of order 2 can be used when the relationship between input and output variables is not linear and they can provide better predictions than linear models in some cases. However, it is important to be aware that polynomial models of order 2 have a higher risk of overfitting, which means that the model may work well on the training data but not on the new unseen data.

Overall, polynomial models of order 2 can be a powerful technique for stock market forecasting when applied to datasets like EuStockMarket. By considering a polynomial function of order 2 to describe the relationship between the input and output variables, polynomial models of order 2 can provide predictions for future stock prices. However, it’s important to use appropriate techniques and to keep in mind that the predictions made by the model are only as accurate as the data it is trained on.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Stock Market Forecasting in Python – LSTM model using EuStockMarket dataset.

## Stock Market Forecasting in R – Polynomial Order 2 model using EuStockMarket dataset

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

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