98 points by code_market_wiz 4 months ago flag hide 21 comments
ml_enthusiast 4 months ago next
Interesting project! Could you share more details about the type of machine learning model you used? Also, what kind of data preprocessing did you do before training the model?
data_scientist 4 months ago next
The model is a long short-term memory (LSTM) network, which is a type of recurrent neural network. I fed in historical stock prices and volume data as input to the model. As for data preprocessing, I used standardization techniques such as mean normalization and applied some feature engineering techniques to extract more meaningful features from the raw data.
data_scientist 4 months ago next
Sure, I extracted trends, moving averages, and other similar statistical features from the raw data. I also experimented with different combinations of features to see how they affect the performance of the model.
ml_beginner 4 months ago prev next
That sounds interesting! Could you explain a bit more about what feature engineering techniques you used?
quant_hunter 4 months ago prev next
LSTM networks have been successful in many time series prediction tasks. However, have you considered using causal convolutional neural networks? I found that they can perform better in some cases, especially with longer sequences.
data_scientist 4 months ago next
Thanks for the suggestion! I will definitely look into it. I am always open to trying new techniques and approaches to improve my models.
algo_trader 4 months ago prev next
I have tried using machine learning models for stock price prediction in the past, but I found that they don't always perform very well. Have you looked into other factors that might be affecting the stock prices, such as macroeconomic trends or industry-specific factors?
data_scientist 4 months ago next
Yes, I have considered those factors as well. I actually included some external data sources in my training data, such as unemployment rates, inflation rates, and other economic indicators. I also experimented with different feature combinations to see how they affect the performance of the model.
deep_learning_guru 4 months ago prev next
I recently read a paper that discussed a new deep learning architecture called temporal convolutional networks (TCNs) that were specifically designed for time series data. They were found to significantly outperform LSTMs and other recurrent neural networks in terms of both accuracy and efficiency.
ml_enthusiast 4 months ago next
Thanks for sharing that! I will definitely check it out. I am always looking for ways to improve my models.
quant_analyst 4 months ago prev next
Have you looked into using other types of machine learning models for stock price prediction, such as decision trees or random forests? I found that they perform better than neural networks in some cases, especially when dealing with small datasets.
data_scientist 4 months ago next
Yes, I have tried using other types of machine learning models as well, including decision trees, random forests, and gradient boosting machines. While they did perform well in some cases, especially with small datasets, I found that LSTMs generally produced better results for this specific task.
crypto_trader 4 months ago prev next
Have you considered using your machine learning model for cryptocurrency price prediction? I find that cryptocurrency prices can be even more volatile than traditional stock prices, making them an interesting challenge for machine learning algorithms.
data_scientist 4 months ago next
Yes, I have considered using my machine learning model for cryptocurrency price prediction as well. In fact, I have done some initial experiments with it, and I found that the model performs quite well. However, the results need to be further validated before I can make any conclusions.
backend_engineer 4 months ago prev next
I'm not very familiar with machine learning, but this project seems really cool. How did you get started with building your machine learning model? Do you have any recommendations for learning resources for beginners?
data_scientist 4 months ago next
I started with some online courses such as Andrew Ng's Machine Learning course on Coursera and Jeremy Howard's Fast.ai course. I also recommend reading books such as Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron and Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Once you have some basic understanding, start building small projects to apply your knowledge in practical scenarios. Good luck!
ml_enthusiast 4 months ago prev next
Thanks for sharing your project! I'm sure many people will find it interesting and informative. Keep up the good work!
hacker 4 months ago prev next
Nice work! Could you open-source your code, so that others can learn from it and contribute to it?
data_scientist 4 months ago next
Sure, I plan to open-source the code in the near future. Stay tuned!
researcher 4 months ago prev next
I'm conducting research in the field of stock price prediction using machine learning algorithms. This project is very relevant to my work. Do you mind sharing some more details about your dataset and evaluation metric?
data_scientist 4 months ago next
Sure, I used historical stock price and volume data for the past 10 years for training and testing my model. I used the root mean squared error (RMSE) metric for evaluating the performance of my model. The RMSE was calculated for each stock's price prediction and then averaged across all stocks. I also used cross-validation to ensure the robustness of the model's performance.