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Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Data Science](https://towardsdatascience.com/category/data-science/) # Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model In this post I show you how to predict stock prices using a forecasting LSTM model [Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/) September 14, 20207 min read ![Figure created by the author.](../images/000.png) Figure created by the author. _**Note from Towards Data Science's editors:**__While we allow independent authors to publish articles in accordance with our_[ _rules and guidelines_](https://towardsdatascience.com/questions-96667b06af5) _, we do not endorse each author's contribution. You should not rely on an author's works without seeking professional advice. See our_[ _Reader Terms_](https://towardsdatascience.com/readers-terms-b5d780a700a4) _for details._ Learn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps. ## 1. Introduction ### 1.1. Time-series & forecasting models Traditionally most machine learning (ML) models use as input features some observations (samples / examples) but there is no **time** **dimension** in the data. **Time-series forecasting** models are the models that are capable to **predict** **future values** based on **previously** **observed** **values**. Time-series forecasting is widely used for **non-stationary data**. **Non-stationary data** are called the data whose statistical properties e.g. the mean and standard deviation are not constant over time but instead, these metrics vary over time. These non-stationary input data (used as input to these models) are usually called **time-series.** Some examples of time-series include the temperature values over time, stock price over time, price of a house over time etc. So, the input is a **signal** (time-series) that is **defined by observations taken sequentially in time**. > A time series is a sequence of observations taken sequentially in time. ![An example of a time-series. Plot created by the author in Python.](../images/001.png) An example of a time-series. Plot created by the author in Python. **Observation: Time-series** data is recorded on a **discrete** **time** **scale**. **Disclaimer** (before we move on): There have been attempts to predict stock prices using time series analysis algorithms, though they still cannot be used to place bets in the real market. This is just a tutorial article that does not intent in any way to "direct" people into buying stocks. * * * ## 2. The LSTM model **Long short-term memory** (**LSTM**) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can not only process single data points (e.g. images), but also entire sequences of data (such as speech or video inputs). **LSTM models** are able to store information over a period of time. > In order words, they have a memory capacity. Remember that LSTM stands for Long Short-Term Memory Model. This characteristic is extremely useful when we deal with Time-Series or Sequential Data. When using an LSTM model we are free and able to decide what information will be stored and what discarded. We do that using the "gates". The deep understanding of the LSTM is outside the scope of this post but if you are interested in learning more, have a look at the references at the end of this post. * * * > **NEW** : After a great deal of hard work and staying behind the scenes for quite a while, we're excited to now offer our expertise through a platform, the "[**Data Science Hub**](https://www.patreon.com/TheDataScienceHub\))" on Patreon (). This hub is our way of providing you with **bespoke consulting services** and comprehensive **responses to all your inquiries** , ranging from Machine Learning to strategic data analytics planning. * * * ## 3. Getting the stock price history data Thanks to **Yahoo finance** we can get the data for **free**. Use the following link to get the stock price history of **TESLA** : [https://finance.yahoo.com/quote/TSLA/history?period1=1436486400&period2=1594339200&interval=1d&filter=history&frequency=1d](https://finance.yahoo.com/quote/TSLA/history?period1=1436486400&period2=1594339200&interval=1d&filter=history&frequency=1d) You should see the following: ![](../images/body-4632f022c0c2ea7a4a71.png) Click on the **Download** and save the **.csv** file locally on your computer. The data are from **2015** till now (**2020**) ! * * * ## 4.Python working example _Modules needed: Keras, Tensorflow, Pandas, Scikit-Learn & Numpy_ We are going to build a **multi-layer LSTM recurrent neural network** to **predict** the **last value of a sequence of values** i.e. the TESLA stock price in this example. Let's **load** the **data** and **inspect** them: text ``` import math import matplotlib.pyplot as plt import keras import pandas as pd import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.layers import Dropout from keras.layers import * from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split from keras.callbacks import EarlyStopping ``` text ``` df=pd.read_csv("TSLA.csv") print('Number of rows and columns:', df.shape) df.head(5) ``` ![Output of the above code](../images/002.png) Output of the above code The next step is to **split** the data into **training** and **test** sets to avoid **overfitting** and to be able to investigate the generalization ability of our model. To learn more about overfitting read this article: > [**Is your model overfitting? Or maybe underfitting? An example using a neural network in python**](https://towardsdatascience.com/is-your-model-overfitting-or-maybe-underfitting-an-example-using-a-neural-network-in-python-4faf155398d2) The target value to be predicted is going to be the "**Close** " stock price value. text ``` training_set = df.iloc[:800, 1:2].values test_set = df.iloc[800:, 1:2].values ``` It's a good idea to **normalize** the data before **model** **fitting**. This will boost the performance. You can read more here for the **Min-Max Scaler** : > [**Everything you need to know about Min-Max normalization in Python**](https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79) Let's build the input features with time **lag of 1 day (lag 1):** text ``` # Feature Scaling sc = MinMaxScaler(feature_range = (0, 1)) training_set_scaled = sc.fit_transform(training_set) ``` text ``` # Creating a data structure with 60 time-steps and 1 output X_train = [] y_train = [] for i in range(60, 800): X_train.append(training_set_scaled[i-60:i, 0]) y_train.append(training_set_scaled[i, 0]) X_train, y_train = np.array(X_train), np.array(y_train) ``` text ``` X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) #(740, 60, 1) ``` We have now **reshaped** the data into the following format (#values, #time-steps, #1 dimensional output). **Now, it's time to build the model.** We will build the **LSTM** with 50 neurons and **4 hidden layers**. Finally, we will assign 1 neuron in the output layer for predicting the normalized stock price. We will use the MSE loss function and the Adam stochastic gradient descent optimizer. **Note** : the following will take some time (~5min). text ``` model = Sequential() ``` text ``` #Adding the first LSTM layer and some Dropout regularisation model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1))) model.add(Dropout(0.2)) ``` text ``` # Adding a second LSTM layer and some Dropout regularisation model.add(LSTM(units = 50, return_sequences = True)) model.add(Dropout(0.2)) ``` text ``` # Adding a third LSTM layer and some Dropout regularisation model.add(LSTM(units = 50, return_sequences = True)) model.add(Dropout(0.2)) ``` text ``` # Adding a fourth LSTM layer and some Dropout regularisation model.add(LSTM(units = 50)) model.add(Dropout(0.2)) ``` text ``` # Adding the output layer model.add(Dense(units = 1)) ``` text ``` # Compiling the RNN model.compile(optimizer = 'adam', loss = 'mean_squared_error') ``` text ``` # Fitting the RNN to the Training set model.fit(X_train, y_train, epochs = 100, batch_size = 32) ``` When the fitting is finished you should see something like this: ![](../images/body-b8af86323cdb9ba77aff.png) Prepare the test data (reshape them): text ``` # Getting the predicted stock price of 2017 dataset_train = df.iloc[:800, 1:2] dataset_test = df.iloc[800:, 1:2] ``` text ``` dataset_total = pd.concat((dataset_train, dataset_test), axis = 0) ``` text ``` inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values ``` text ``` inputs = inputs.reshape(-1,1) inputs = sc.transform(inputs) X_test = [] for i in range(60, 519): X_test.append(inputs[i-60:i, 0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) ``` text ``` print(X_test.shape) # (459, 60, 1) ``` **Make Predictions using the test set** text ``` predicted_stock_price = model.predict(X_test) predicted_stock_price = sc.inverse_transform(predicted_stock_price) ``` Let's visualize the results now: text ``` # Visualising the results plt.plot(df.loc[800:, 'Date'],dataset_test.values, color = 'red', label = 'Real TESLA Stock Price') plt.plot(df.loc[800:, 'Date'],predicted_stock_price, color = 'blue', label = 'Predicted TESLA Stock Price') plt.xticks(np.arange(0,459,50)) plt.title('TESLA Stock Price Prediction') plt.xlabel('Time') plt.ylabel('TESLA Stock Price') plt.legend() plt.show() ``` ## 5. Results **Using a lag of 1 (i.e. step of one day):** ![](../images/body-d74003d426b1baf9eb8f.png) **Observation** : Huge drop in March 2020 due to the COVID-19 lockdown ! We can clearly see that our model performed very good. It is able to accuretly follow most of the unexcepted jumps/drops however, for the most recent date stamps, we can see that the model expected (predicted) lower values compared to the real values of the stock price. * * * ## A note about the lag **The initial selected lag in this article was 1 i.e. using a step of 1 day**. This can be easily changed by altering the code that builds the 3D inputs. **Example** : One can change the following 2 blocks of code: text ``` X_train = [] y_train = [] for i in range(60, 800): X_train.append(training_set_scaled[i-60:i, 0]) y_train.append(training_set_scaled[i, 0]) ``` and text ``` X_test = [] y_test = [] for i in range(60, 519): X_test.append(inputs[i-60:i, 0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) ``` **with the following new code:** text ``` X_train = [] y_train = [] for i in range(60, 800): X_train.append(training_set_scaled[i-50:i, 0]) y_train.append(training_set_scaled[i, 0]) ``` **and** text ``` X_test = [] y_test = [] for i in range(60, 519): X_test.append(inputs[i-50:i, 0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) ``` In that case the results look like this: ![](../images/body-5daeaae53f0c340208f5.png) * * * That's all folks ! Hope you liked this article! ### Have a look at my Facebook Prophet model that I used to predict the GOOGLE stock price in another article. > [**Time-Series Forecasting: Predicting Stock Prices Using Facebook's Prophet Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-facebooks-prophet-model-9ee1657132b5) ### Check also my recent article using an ARIMA model: > [**Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70) * * * ## References [1] [2] * * * ## Stay tuned & support this effort If you liked and found this article useful, **follow** me to be able to see all my new posts. Questions? Post them as a comment and I will reply as soon as possible. * * * ## Latest posts > [**Time-Series Forecasting: Predicting Stock Prices Using Facebook's Prophet Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-facebooks-prophet-model-9ee1657132b5) > [**The Best FREE Data Science Resources: FREE Books & Online Courses**](https://medium.com/@seralouk/the-best-free-data-science-resources-free-books-online-courses-9c4a2df194e5) > [**ROC Curve Explained using a COVID-19 hypothetical example: Binary & Multi-Class Classification...**](https://towardsdatascience.com/roc-curve-explained-using-a-covid-19-hypothetical-example-binary-multi-class-classification-bab188ea869c) > [**Support Vector Machines (SVM) clearly explained: A python tutorial for classification problems...**](https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8) > [**PCA clearly explained - How, when, why to use it and feature importance: A guide in Python**](https://towardsdatascience.com/pca-clearly-explained-how-when-why-to-use-it-and-feature-importance-a-guide-in-python-7c274582c37e) > [**Everything you need to know about Min-Max normalization in Python**](https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79) > [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832) * * * ## Get in touch with me * **LinkedIn** : * **ResearchGate** : * **EPFL** **profile** : * **Stack** **Overflow** : ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. 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