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Time Series Forecasting with LSTM Neural Networks using TensorFlow


Time series forecasting is an important field of study that aims to predict future values of a time-dependent variable based on its past values. Numerous industries can use this method, including banking, economics, weather forecasting, and engineering.

Recurrent neural networks (RNNs) and long short-term memory (LSTM), two deep learning techniques, have recently become effective time series forecasting tools.

By the conclusion of this article, readers will understand how to utilize TensorFlow and LSTM neural networks for time series forecasting, and they will have a real-world example.

Data Preprocessing

Cleaning and preprocessing time series data is essential in time series analysis. In this section, we will explain how to clean and preprocess time series data and how to prepare it for model training using TensorFlow.

  • Data Cleaning: The first step in cleaning time series data is to remove any outliers, missing values, or inconsistencies. Inconsistencies can be due to errors in data entry or data collection. It is important to identify and remove these data points as they can significantly impact the accuracy of the model.
  • Data Resampling: The next step is to resample the data to a fixed frequency once the data has been cleaned. This involves converting irregularly sampled data to a fixed frequency, such as daily, weekly, or monthly. Feature Engineering: Feature engineering involves selecting and creating features that can be used as input to the model.
  • Normalization: Normalization is scaling the data to a common range. This is important as it can improve the convergence of the model and prevent large weights from dominating the optimization process. Common techniques for normalization include min-max scaling and z-score normalization.
  • Splitting the Data: Finally, the data is split into training, validation, and testing sets.

Once the data has been preprocessed, it can be fed into the LSTM model for training. It is important to note that the quality of the preprocessed data can significantly impact the accuracy of the model. Therefore, it is important to spend time cleaning and preprocessing the data to ensure the best possible performance of the model.

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LSTM Modeling

Due to its capacity to recognize long-term relationships in the data, LSTM (Long Short-Term Memory) neural networks are a form of recurrent neural network (RNN) frequently employed in time series forecasting, including stock market prediction. In this section, we will detail the architecture of an LSTM neural network and explain how to build, train, and optimize a model using TensorFlow for stock market prediction.

Architecture of an LSTM Neural Network for Stock Market Prediction: The architecture of an LSTM neural network for stock market prediction includes the following layers:

  • Input Layer: The input layer takes past stock prices and other relevant features such as volume, technical indicators, or news sentiment.
  • LSTM Layer: The LSTM layer processes the input sequence and captures the long-term dependencies in the data.
  • Dropout Layer: The dropout layer is used for regularization and helps prevent overfitting.
  • Dense Layer: The dense layer is the output layer of the network and produces the predicted stock price.

Building and Training an LSTM Neural Network for Stock Market Prediction using TensorFlow: Here are the steps to build and train an LSTM neural network using TensorFlow for stock market prediction:

  • Data Preparation: Prepare the data by collecting the historical stock prices and any relevant features.
  • Data Preprocessing: Preprocess the data by scaling the input features and creating a sliding window of the past prices to use as input for the LSTM layer.
  • Define the Model Architecture: Define the architecture of the LSTM model using TensorFlow. This includes defining the number of LSTM layers, the number of cells in each layer, and the number of input features.
  • Compile the Model: Compile the model using TensorFlow. This involves specifying the loss function, the optimizer, and the evaluation metrics.
  • Fit the Model: Fit the model using TensorFlow. This involves specifying the training data, the validation data, the batch size, the number of epochs, and the callbacks.
  • Evaluate the Model: Utilize the testing data to assess the model’s performance. This requires calculating evaluation metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Optimizing an LSTM Neural Network for Stock Market Prediction using TensorFlow: Here are some techniques to optimize the performance of an LSTM neural network using TensorFlow for stock market prediction:

  • Hyperparameter Tuning: The model’s performance may be significantly impacted by hyperparameters like the learning rate, the number of LSTM layers, and the number of cells in each layer. Therefore, tuning these hyperparameters to find the best possible configuration is important.
  • Regularization: To avoid overfitting and increase the generalizability of the model, regularisation methods like L1 regularisation, L2 regularisation, and dropout can be applied.
  • Ensembling: By integrating the predictions of many models, ensemble methods like bagging and boosting may be utilized to enhance the performance of the model.


We’ll give the example of forecasting the S&P 500 index’s daily closing price using past price information.

The dataset used for this example consists of the daily closing price of the S&P 500 index from January 1st, 2000 to December 31st, 2021. The data was split into training data (January 1st, 2000 to December 31st, 2018) and testing data (January 1st, 2019 to December 31st, 2021).

After preparing the data and building the LSTM model using TensorFlow, the model was trained on the training data for 100 epochs with a batch size of 64. Using the testing data, the model was then assessed using pertinent metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results of the LSTM model on the stock market dataset are as follows:

  • MAE: 33.03
  • RMSE: 45.78

The MAE of 33.03 indicates that, on average, the predicted daily closing price of the S&P 500 index is off by $33.03 compared to the actual closing price. Similarly, the RMSE of 45.78 indicates that the standard deviation of the errors between the predicted and actual closing prices is $45.78.


The example study above used an LSTM neural network with TensorFlow to attempt to forecast the daily closing price of the S&P 500 index. The key findings of the case study are as follows:

The S&P 500 index daily closing price may be predicted by the LSTM model with an average MAE of $33.03 and an RMSE of $45.78.

The LSTM model captured the underlying patterns and trends in the stock market data, which suggests its potential usefulness in stock market prediction.

Drop a query if you have any questions regarding LSTM neural network with TensorFlow and we will get back to you quickly.

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1. What are the key challenges in stock market prediction using LSTM Neural Networks?

ANS: – Stock market prediction is inherently challenging due to its non-linear and volatile nature. LSTM Neural Networks may struggle with extreme market fluctuations and unexpected events that can impact stock prices.

2. Are there any limitations to using LSTM Neural Networks for stock market prediction?

ANS: – LSTM Neural Networks require substantial historical data to capture long-term dependencies accurately. They may also struggle to predict rare events or sudden market shifts.

WRITTEN BY Sagar Malik

Sagar Malik works as a Research Associate - Tech consulting and holds a degree in Computer Science. He is interested in Machine Learning and its applications in the real world. He helps the client in better decision-making using data.



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