Stock Market Forecasting in R – Auto ARIMA model using EuStockMarket dataset

Stock Market Forecasting in R – Auto ARIMA 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 the Auto ARIMA model, which is a type of time series forecasting model that is used to predict future values based on historical patterns.

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

The Auto ARIMA model is a combination of three models: an autoregressive model, a moving average model and a differencing model which can adaptively identify the best combination of the three models.

The process of building a Auto ARIMA model typically involves the following steps:

  1. Collecting and cleaning the data. This includes acquiring the EuStockMarket dataset and preparing it for analysis.
  2. Decomposition of time series: This step is optional but it is useful to understand the trend, seasonality and residual components of the time series.
  3. Choosing an appropriate Auto ARIMA model. This could be a simple Auto ARIMA model or multiple Auto ARIMA model.
  4. Training the model. This includes estimating the parameters of the model, such as the coefficients, using the historical data.
  5. Forecasting. This includes using the trained model to predict future stock prices.
  6. 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, Auto ARIMA model is suitable when the time series data is non-stationary and has a clear trend and seasonality. Auto ARIMA model can provide better predictions than linear models in some cases. However, it is important to be aware that Auto ARIMA model 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, Auto ARIMA model can be a powerful technique for stock market forecasting when applied to datasets like EuStockMarket. By considering a Auto ARIMA model to describe the relationship between the input and output variables, Auto ARIMA model 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 R – Auto ARIMA model using EuStockMarket dataset.



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