{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Serafeim Loukas, PhD",
  "title_listed": "LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model",
  "keywords": "",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f/",
  "publish_date": "2020-07-10",
  "corpus_stage": "",
  "notes": "",
  "id": "d1836aa84cc8",
  "collected_at": "2026-10-02T08:18:24",
  "title": "LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Stock Market](https://towardsdatascience.com/category/stock-market/)\n# LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model\nIn this post I show you how to predict stock prices using a forecasting LSTM model\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nJuly 10, 20208 min read\n# Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model\n![Figure created by the author.](../images/000.png)\nFigure created by the author.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n## 1. Introduction\n### 1.1. Time-series & forecasting models\nTraditionally most machine learning (ML) models use as input features some observations (samples / examples) but there is no **time** **dimension** in the data.\n**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.\nThese 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**.\n> A time series is a sequence of observations taken sequentially in time.\n![An example of a time-series. Plot created by the author in Python.](../images/001.png)\nAn example of a time-series. Plot created by the author in Python.\n**Observation: Time-series** data is recorded on a **discrete** **time** **scale**.\n**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.\n* * *\n  * My mailing list in just 5 seconds: <https://seralouk.medium.com/subscribe>\n  * Become a member and support me:<https://seralouk.medium.com/membership>\n  * **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 (<https://www.patreon.com/TheDataScienceHub>). 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\n\n\n* * *\n## 2. The LSTM model\n**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).\n**LSTM models** are able to store information over a period of time.\n> In order words, they have a memory capacity. Remember that LSTM stands for **Long Short-Term Memory Model.**\nThis 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.\n* * *\nIf you want to learn Data Science by yourself with the support of interactive roadmaps and an active learning community have a look at this resource: <https://aigents.co/learn>\n* * *\n## 3. Getting the stock price history data\nThanks 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)\nYou should see the following:\n![](../images/body-4632f022c0c2ea7a4a71.png)\nClick on the **Download** and save the **.csv** file locally on your computer.\nThe data are from **2015** till now (**2020**) !\n* * *\n## 4.Python working example\n_Modules needed: Keras, Tensorflow, Pandas, Scikit-Learn & Numpy_\nWe 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.\nLet's **load** the **data** and **inspect** them:\ntext\n\n```\nimport math\nimport matplotlib.pyplot as plt\nimport keras\nimport pandas as pd\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\nfrom keras.layers import Dropout\nfrom keras.layers import *\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.model_selection import train_test_split\nfrom keras.callbacks import EarlyStopping\n```\n\ntext\n\n```\ndf=pd.read_csv(\"TSLA.csv\")\nprint('Number of rows and columns:', df.shape)\ndf.head(5)\n```\n\n![Output of the above code](../images/002.png)\nOutput of the above code\nThe 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:\n> [**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)\nThe target value to be predicted is going to be the \"**Close** \" stock price value.\ntext\n\n```\ntraining_set = df.iloc[:800, 1:2].values\ntest_set = df.iloc[800:, 1:2].values\n```\n\nIt'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** :\n> [**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)\nLet's build the input features with time **lag of 1 day (lag 1):**\ntext\n\n```\n# Feature Scaling\nsc = MinMaxScaler(feature_range = (0, 1))\ntraining_set_scaled = sc.fit_transform(training_set)\n```\n\ntext\n\n```\n# Creating a data structure with 60 time-steps and 1 output\nX_train = []\ny_train = []\nfor i in range(60, 800):\n    X_train.append(training_set_scaled[i-60:i, 0])\n    y_train.append(training_set_scaled[i, 0])\nX_train, y_train = np.array(X_train), np.array(y_train)\n```\n\ntext\n\n```\nX_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n#(740, 60, 1)\n```\n\nWe have now **reshaped** the data into the following format (#values, #time-steps, #1 dimensional output).\n**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.\n**Note** : the following will take some time (~5min).\ntext\n\n```\nmodel = Sequential()\n```\n\ntext\n\n```\n#Adding the first LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding a second LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50, return_sequences = True))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding a third LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50, return_sequences = True))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding a fourth LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding the output layer\nmodel.add(Dense(units = 1))\n\n# Compiling the RNN\nmodel.compile(optimizer = 'adam', loss = 'mean_squared_error')\n\n# Fitting the RNN to the Training set\nmodel.fit(X_train, y_train, epochs = 100, batch_size = 32)\n```\n\nWhen the fitting is finished you should see something like this:\n![](../images/body-b8af86323cdb9ba77aff.png)\nPrepare the test data (reshape them):\ntext\n\n```\n# Getting the predicted stock price of 2017\ndataset_train = df.iloc[:800, 1:2]\ndataset_test = df.iloc[800:, 1:2]\n```\n\ntext\n\n```\ndataset_total = pd.concat((dataset_train, dataset_test), axis = 0)\n```\n\ntext\n\n```\ninputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values\n```\n\ntext\n\n```\ninputs = inputs.reshape(-1,1)\ninputs = sc.transform(inputs)\nX_test = []\nfor i in range(60, 519):\n    X_test.append(inputs[i-60:i, 0])\nX_test = np.array(X_test)\nX_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n```\n\ntext\n\n```\nprint(X_test.shape)\n# (459, 60, 1)\n```\n\n**Make Predictions using the test set**\ntext\n\n```\npredicted_stock_price = model.predict(X_test)\npredicted_stock_price = sc.inverse_transform(predicted_stock_price)\n```\n\nLet's visualize the results now:\ntext\n\n```\n# Visualising the results\nplt.plot(df.loc[800:, 'Date'],dataset_test.values, color = 'red', label = 'Real TESLA Stock Price')\nplt.plot(df.loc[800:, 'Date'],predicted_stock_price, color = 'blue', label = 'Predicted TESLA Stock Price')\nplt.xticks(np.arange(0,459,50))\nplt.title('TESLA Stock Price Prediction')\nplt.xlabel('Time')\nplt.ylabel('TESLA Stock Price')\nplt.legend()\nplt.show()\n```\n\n## 5. Results\n**Using a lag of 1 (i.e. step of one day):**\n![](../images/body-d74003d426b1baf9eb8f.png)\n**Observation** : Huge drop in March 2020 due to the COVID-19 lockdown !\nWe 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.\n* * *\nIf you're passionate about diving deeper into machine learning with Python and sklearn, I highly recommend checking out [**this book**](https://amzn.to/3t4YB2d); it's a game-changer in breaking down complex topics into digestible insights.\n> [**Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques...**](https://amzn.to/3t4YB2d)\n* * *\n## A note about the lag\n**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.\n**Example** : One can change the following 2 blocks of code:\ntext\n\n```\nX_train = []\ny_train = []\nfor i in range(60, 800):\n    X_train.append(training_set_scaled[i-60:i, 0])\n    y_train.append(training_set_scaled[i, 0])\n```\n\nand\ntext\n\n```\nX_test = []\ny_test = []\nfor i in range(60, 519):\n    X_test.append(inputs[i-60:i, 0])\nX_test = np.array(X_test)\nX_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n```\n\n**with the following new code:**\ntext\n\n```\nX_train = []\ny_train = []\nfor i in range(60, 800):\n    X_train.append(training_set_scaled[i-50:i, 0])\n    y_train.append(training_set_scaled[i, 0])\n```\n\n**and**\ntext\n\n```\nX_test = []\ny_test = []\nfor i in range(60, 519):\n    X_test.append(inputs[i-50:i, 0])\nX_test = np.array(X_test)\nX_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n```\n\nIn that case the results look like this:\n![](../images/body-5daeaae53f0c340208f5.png)\n* * *\nThat's all folks ! Hope you liked this article!\n* * *\n  * **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 (<https://www.patreon.com/TheDataScienceHub>). 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.\n\n\n* * *\nMore readings:\n### **Have a look at my Facebook Prophet model that I used to predict the GOOGLE stock price in another article.**\n> [**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)\n### Check also my recent article using an ARIMA model:\n> [**Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70)\n* * *\n## References\n[1] <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>\n[2] <https://en.wikipedia.org/wiki/Long_short-term_memory>\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? Post them as a comment and I will reply as soon as possible.\n* * *\n## Latest posts\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832)\n* * *\n## Get in touch with me\n  * **LinkedIn** : <https://www.linkedin.com/in/serafeim-loukas/>\n  * **ResearchGate** : <https://www.researchgate.net/profile/Serafeim_Loukas>\n  * **EPFL** **profile** : <https://people.epfl.ch/serafeim.loukas>\n  * **Stack** **Overflow** : <https://stackoverflow.com/users/5025009/seralouk>\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Stock Market](https://towardsdatascience.com/category/stock-market/)\n# LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model\nIn this post I show you how to predict stock prices using a forecasting LSTM model\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nJuly 10, 20208 min read\n# Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1sGz5qx3EefIG7CJ4mfZBOg.png)\nFigure created by the author.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n## 1. Introduction\n### 1.1. Time-series & forecasting models\nTraditionally most machine learning (ML) models use as input features some observations (samples / examples) but there is no **time** **dimension** in the data.\n**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.\nThese 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**.\n> A time series is a sequence of observations taken sequentially in time.\n![An example of a time-series. Plot created by the author in Python.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1SULcaLxjHsAVmhxG7Xo0cA.png)\nAn example of a time-series. Plot created by the author in Python.\n**Observation: Time-series** data is recorded on a **discrete** **time** **scale**.\n**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.\n* * *\n  * My mailing list in just 5 seconds: <https://seralouk.medium.com/subscribe>\n  * Become a member and support me:<https://seralouk.medium.com/membership>\n  * **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 (<https://www.patreon.com/TheDataScienceHub>). 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\n\n\n* * *\n## 2. The LSTM model\n**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).\n**LSTM models** are able to store information over a period of time.\n> In order words, they have a memory capacity. Remember that LSTM stands for **Long Short-Term Memory Model.**\nThis 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.\n* * *\nIf you want to learn Data Science by yourself with the support of interactive roadmaps and an active learning community have a look at this resource: <https://aigents.co/learn>\n* * *\n## 3. Getting the stock price history data\nThanks 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)\nYou should see the following:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1ClLXfFKG6zL3RsZI1jrZaw.png)\nClick on the **Download** and save the **.csv** file locally on your computer.\nThe data are from **2015** till now (**2020**) !\n* * *\n## 4.Python working example\n_Modules needed: Keras, Tensorflow, Pandas, Scikit-Learn & Numpy_\nWe 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.\nLet's **load** the **data** and **inspect** them:\ntext\n\n```\nimport math\nimport matplotlib.pyplot as plt\nimport keras\nimport pandas as pd\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\nfrom keras.layers import Dropout\nfrom keras.layers import *\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.model_selection import train_test_split\nfrom keras.callbacks import EarlyStopping\n```\n\ntext\n\n```\ndf=pd.read_csv(\"TSLA.csv\")\nprint('Number of rows and columns:', df.shape)\ndf.head(5)\n```\n\n![Output of the above code](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1fB29WXKDO6Y8zSsj87OTBg.png)\nOutput of the above code\nThe 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:\n> [**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)\nThe target value to be predicted is going to be the \"**Close** \" stock price value.\ntext\n\n```\ntraining_set = df.iloc[:800, 1:2].values\ntest_set = df.iloc[800:, 1:2].values\n```\n\nIt'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** :\n> [**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)\nLet's build the input features with time **lag of 1 day (lag 1):**\ntext\n\n```\n# Feature Scaling\nsc = MinMaxScaler(feature_range = (0, 1))\ntraining_set_scaled = sc.fit_transform(training_set)\n```\n\ntext\n\n```\n# Creating a data structure with 60 time-steps and 1 output\nX_train = []\ny_train = []\nfor i in range(60, 800):\n    X_train.append(training_set_scaled[i-60:i, 0])\n    y_train.append(training_set_scaled[i, 0])\nX_train, y_train = np.array(X_train), np.array(y_train)\n```\n\ntext\n\n```\nX_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n#(740, 60, 1)\n```\n\nWe have now **reshaped** the data into the following format (#values, #time-steps, #1 dimensional output).\n**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.\n**Note** : the following will take some time (~5min).\ntext\n\n```\nmodel = Sequential()\n```\n\ntext\n\n```\n#Adding the first LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding a second LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50, return_sequences = True))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding a third LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50, return_sequences = True))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding a fourth LSTM layer and some Dropout regularisation\nmodel.add(LSTM(units = 50))\nmodel.add(Dropout(0.2))\n```\n\ntext\n\n```\n# Adding the output layer\nmodel.add(Dense(units = 1))\n\n# Compiling the RNN\nmodel.compile(optimizer = 'adam', loss = 'mean_squared_error')\n\n# Fitting the RNN to the Training set\nmodel.fit(X_train, y_train, epochs = 100, batch_size = 32)\n```\n\nWhen the fitting is finished you should see something like this:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1fmQ6ePlJjvRoRAJCFGDbew.png)\nPrepare the test data (reshape them):\ntext\n\n```\n# Getting the predicted stock price of 2017\ndataset_train = df.iloc[:800, 1:2]\ndataset_test = df.iloc[800:, 1:2]\n```\n\ntext\n\n```\ndataset_total = pd.concat((dataset_train, dataset_test), axis = 0)\n```\n\ntext\n\n```\ninputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values\n```\n\ntext\n\n```\ninputs = inputs.reshape(-1,1)\ninputs = sc.transform(inputs)\nX_test = []\nfor i in range(60, 519):\n    X_test.append(inputs[i-60:i, 0])\nX_test = np.array(X_test)\nX_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n```\n\ntext\n\n```\nprint(X_test.shape)\n# (459, 60, 1)\n```\n\n**Make Predictions using the test set**\ntext\n\n```\npredicted_stock_price = model.predict(X_test)\npredicted_stock_price = sc.inverse_transform(predicted_stock_price)\n```\n\nLet's visualize the results now:\ntext\n\n```\n# Visualising the results\nplt.plot(df.loc[800:, 'Date'],dataset_test.values, color = 'red', label = 'Real TESLA Stock Price')\nplt.plot(df.loc[800:, 'Date'],predicted_stock_price, color = 'blue', label = 'Predicted TESLA Stock Price')\nplt.xticks(np.arange(0,459,50))\nplt.title('TESLA Stock Price Prediction')\nplt.xlabel('Time')\nplt.ylabel('TESLA Stock Price')\nplt.legend()\nplt.show()\n```\n\n## 5. Results\n**Using a lag of 1 (i.e. step of one day):**\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1t-NC8uK5gEoq4ZN_iM9xlw.png)\n**Observation** : Huge drop in March 2020 due to the COVID-19 lockdown !\nWe 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.\n* * *\nIf you're passionate about diving deeper into machine learning with Python and sklearn, I highly recommend checking out [**this book**](https://amzn.to/3t4YB2d); it's a game-changer in breaking down complex topics into digestible insights.\n> [**Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques...**](https://amzn.to/3t4YB2d)\n* * *\n## A note about the lag\n**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.\n**Example** : One can change the following 2 blocks of code:\ntext\n\n```\nX_train = []\ny_train = []\nfor i in range(60, 800):\n    X_train.append(training_set_scaled[i-60:i, 0])\n    y_train.append(training_set_scaled[i, 0])\n```\n\nand\ntext\n\n```\nX_test = []\ny_test = []\nfor i in range(60, 519):\n    X_test.append(inputs[i-60:i, 0])\nX_test = np.array(X_test)\nX_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n```\n\n**with the following new code:**\ntext\n\n```\nX_train = []\ny_train = []\nfor i in range(60, 800):\n    X_train.append(training_set_scaled[i-50:i, 0])\n    y_train.append(training_set_scaled[i, 0])\n```\n\n**and**\ntext\n\n```\nX_test = []\ny_test = []\nfor i in range(60, 519):\n    X_test.append(inputs[i-50:i, 0])\nX_test = np.array(X_test)\nX_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n```\n\nIn that case the results look like this:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1ywlBzNk1gQUj2JVsLJO2Gw.png)\n* * *\nThat's all folks ! Hope you liked this article!\n* * *\n  * **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 (<https://www.patreon.com/TheDataScienceHub>). 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.\n\n\n* * *\nMore readings:\n### **Have a look at my Facebook Prophet model that I used to predict the GOOGLE stock price in another article.**\n> [**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)\n### Check also my recent article using an ARIMA model:\n> [**Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70)\n* * *\n## References\n[1] <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>\n[2] <https://en.wikipedia.org/wiki/Long_short-term_memory>\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? Post them as a comment and I will reply as soon as possible.\n* * *\n## Latest posts\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**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)\n> [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832)\n* * *\n## Get in touch with me\n  * **LinkedIn** : <https://www.linkedin.com/in/serafeim-loukas/>\n  * **ResearchGate** : <https://www.researchgate.net/profile/Serafeim_Loukas>\n  * **EPFL** **profile** : <https://people.epfl.ch/serafeim.loukas>\n  * **Stack** **Overflow** : <https://stackoverflow.com/users/5025009/seralouk>\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657496994_4frvt5.png)\nWritten By\n## [Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\n[See all from Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\n[Forecasting](https://towardsdatascience.com/tag/forecasting/)[Lstm](https://towardsdatascience.com/tag/lstm/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Recurrent Neural Network](https://towardsdatascience.com/tag/recurrent-neural-network/)[Stock Market](https://towardsdatascience.com/tag/stock-market/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Flstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f%2F&title=LSTM%20Time-Series%20Forecasting%3A%20Predicting%20Stock%20Prices%20Using%20An%20LSTM%20Model)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Flstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f%2F&title=LSTM%20Time-Series%20Forecasting%3A%20Predicting%20Stock%20Prices%20Using%20An%20LSTM%20Model)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Flstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f%2F&text=LSTM%20Time-Series%20Forecasting%3A%20Predicting%20Stock%20Prices%20Using%20An%20LSTM%20Model)\n\n\nTowards Data Science is a community publication. 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