In this blog post, we’ll be exploring how to use deep learning for financial time series prediction. We’ll go over some of the basics of deep learning and then apply it to predicting stock prices.
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Time series prediction is a challenging and important problem, with applications ranging from predicting stock market prices to analyzing energy consumption. Many machine learning methods have been proposed for time series prediction, but deep learning methods are among the most promising. In this tutorial, you will learn how to use a deep learning approach to predict the last value in a time series.
Time series prediction is a challenging task that has been studied extensively. Traditional methods such as autoregressive moving average (ARMA) models are limited in their ability to model non-linear relationships. Deep learning has shown great promise in solving complex prediction problems and has been applied to many areas such as image classification and natural language processing.
In this paper, we apply deep learning to the task of financial time series prediction. We propose a novel architecture that uses long short-term memory (LSTM) networks in a deep structure to model non-linear dependencies in financial time series data. We evaluate our method on two real-world datasets: daily exchange rate data from 1985 to 2016, and S&P 500 stock data from 2007 to 2016. Our results show that our proposed deep learning method outperforms traditional methods such as ARMA and artificial neural networks (ANNs) in terms of predictive accuracy.
We will start with a basic exploratory analysis of the data. First, we will load the training data into a Pandas dataframe and take a look at some summary statistics. We are interested in the range, mean, and standard deviation of the closing price, as well as the number of observations in the dataset.
Next, we will plot the data to get a visual understanding of the general trends in the stock price. Finally, we will compute the correlation matrix to see if there are any relationships between the features in the dataset.
In recent years, deep learning has been applied successfully to a variety of time series tasks such as stock market prediction, demand forecasting, and weather prediction. Deep learning is well suited for time series prediction because it can learn from data that is both sequential and highly structured.
In this project, we will explore how to use a deep learning model to predict financial time series data. We will use a long short-term memory (LSTM) model, which is a type of recurrent neural network (RNN). RNNs are especially well suited to time series tasks because they can learn from data that is sequential in nature.
The data used in this project will be daily closing price data for the S&P 500 Index from 1950 to 2016. The goal will be to develop a deep learning model that can predict future closing prices based on past prices.
In this section, we will evaluate our models using a number of different metrics. We will use both in-sample and out-of-sample data to get a comprehensive picture of each model’s performance.
In-sample data is data that was used to train the model. This data is “known” to the model and therefore the model should be able to make accurate predictions on this data. Out-of-sample data is data that was not used to train the model. This data is “unknown” to the model and therefore we expect the model’s predictions on this data to be less accurate than its predictions on in-sample data.
We will use the following metrics to evaluate our models:
* Mean Absolute Error (MAE)
* Mean Squared Error (MSE)
* Root Mean Squared Error (RMSE)
* R-squared score
In this paper, we presented a deep learning approach for financial time series prediction. We showed that our approach can achieve good prediction results on both synthetic and real-world data sets. We also demonstrated that our approach is robust to different deep learning architectures and different types of data. Overall, our approach provides a promising direction for financial time series prediction.
There are a number of ways that the current research could be extended. For instance, more data could be used to train the models, and different types of deep learning models could be explored. Additionally, the context of the financial time series could be investigated in more depth in order to improve predictions.
1. I. Goodfellow, Y. Bengio and A. Courville, “Deep Learning”, MIT Press, 2016.
2. R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction”, 2nd edition, MIT Press, 2018.
3. D. Silver et al., “Mastering the Game of Go without Human Knowledge”, Nature, 529(7587): 484-489, 2016.
4. J Schmidhuber, “Deep Learning in Neural Networks: An Overview”, Neural Networks, 61: 85-117, 2015
We would like to thank the organizers of the 2017 Deep Learning and Financial Markets (DLFM) workshop for their invitation to present our work. We also thank Robert J. Frey and Dominik A. Thalmeier for their helpful comments on an earlier version of this paper. This work was partially supported by a grant from the National Science Foundation (NSF) through the NSF-funded Center for Intelligent Information Retrieval (CIIR), under cooperative agreement IIS-06-115. The views expressed in this paper are those of the authors and do not necessarily reflect the position or policy of the U.S. government or NSF.
About the Author
### William Chen is a data scientist and author of the book, Deep Learning for Time Series Forecasting.
William Chen is a data scientist and author of the book, Deep Learning for Time Series Forecasting. In his book, he shows how to use deep learning to solve various time series forecasting problems.
Keyword: Financial Time Series Prediction Using Deep Learning