Master stock price prediction using LSTM Neural Networks

Master stock price prediction using LSTM Neural Networks

Table of Contents

1. Introduction
2. Grabbing the Dataset
3. Preprocessing the Data
4. Converting the Date Column
5. Creating Windowed Data
6. Splitting the Data
7. Building the LSTM Model
8. Training the LSTM Model
9. Evaluating the Model
10. Recursive Predictions
11. Conclusion

Introduction

Welcome to the tutorial on forecasting Microsoft stock using LSTM neural networks. In this tutorial, we will walk through the process of predicting the stock prices of Microsoft using LSTM neural networks. This project is a great addition to your resume, as it demonstrates your ability to work with time series data and implement deep learning models.

Grabbing the Dataset

To begin, we need to obtain the dataset. We can get the dataset from Yahoo Finance by following the provided link. Once on the Microsoft Corporation stock page, we can change the time period to "Max" and click "Apply" to get all the available information. We need to download the dataset in CSV format and then import it into our working environment.

Preprocessing the Data

After downloading the dataset, we need to preprocess it before feeding it into our LSTM model. First, we will import the necessary libraries, including Pandas for data manipulation. We will Read the CSV file using the read_csv() function and store the data in a Pandas DataFrame. The DataFrame will contain information such as the date, opening price, highest price, lowest price, closing price, and trading volume.

Converting the Date Column

Before proceeding further, we need to convert the date column from a STRING format to a date format. Currently, the date column is stored as a string with the year, month, and day separated by hyphens. We will Create a function called string_to_datetime() to convert the strings to datetime objects. This function will split the date string, extract the year, month, and day, convert them to integers, and then return a datetime object.

Creating Windowed Data

To train our LSTM model, we need to convert the dataset into a Supervised learning problem. This can be done by creating a windowed dataset, where each input consists of the prices of the previous three days, and the corresponding output is the price of the next day. We will define a function called define_df_to_windowed_df() which takes the dataset, the first date string, the last date string, and an optional parameter for the window size. This function will return a windowed DataFrame with the input and output values.

Splitting the Data

Before training our model, we need to split the data into training, validation, and testing partitions. The training data will be used to train the model, the validation data will be used to monitor the model's performance during training, and the testing data will be used to evaluate the final model. We will use a split ratio of 80% for training, 10% for validation, and 10% for testing. The data will be split Based on the dates.

Building the LSTM Model

Now, it's time to build our LSTM model. We will be using the Keras library, which provides a simple and convenient way to define and train deep learning models. Our model will consist of an LSTM layer with 64 neurons, followed by two dense layers with 32 neurons each. The activation function for the dense layers will be Rectified Linear Unit (ReLU), which is commonly used in deep learning models. The output layer will consist of a single neuron, as We Are only forecasting the closing price.

Training the LSTM Model

With our model architecture defined, we can now train the LSTM model using the training data. We will compile the model by specifying the loss function, optimizer, and metrics. The loss function we will minimize is the mean squared error (MSE), as it is commonly used for regression problems. The optimizer we will use is Adam, which is a popular choice for training deep learning models. Additionally, we will track the mean absolute error (MAE) as a metric to evaluate the model's performance.

Evaluating the Model

After training the LSTM model, we need to evaluate its performance on the validation and testing data. We will plot the predicted values against the actual values to visually assess the model's accuracy. This will help us understand how well the model is capturing the Patterns and trends in the data. We will plot the graphs separately for the training, validation, and testing datasets to compare the model's performance on each.

Recursive Predictions

In addition to predicting the stock prices based on the observed values, we can also use the trained model to make recursive predictions for future dates. This means we can feed the model's predictions back into the model as input to make predictions for the next day, and so on. We will create a function called recursive_predictions() to perform recursive predictions. The function will iterate over the desired future dates, generate predictions based on the previous observations, and update the input window accordingly.

Conclusion

In this tutorial, we have learned how to forecast Microsoft stock prices using LSTM neural networks. We began by grabbing the dataset from Yahoo Finance and preprocessing the data. We then converted the date column and created a windowed dataset for training the LSTM model. After splitting the data into training, validation, and testing partitions, we built and trained the LSTM model. Finally, we evaluated the model's performance and made recursive predictions for future dates.

If You followed along with the tutorial, you should now have a good understanding of how to use LSTM neural networks for time series forecasting. This is a valuable skill that can be applied to various domains and can help you make informed decisions in the financial market.