Stock Prediction Webapp

I've developed a stock prediction web application that serves as a platform for comparing the performance of two distinct models: the LSTM neural network and Meta Prophet. This app allows users to input stock data and observe how each model predicts future stock prices. By doing so, it helps highlight the differences in predictive capabilities and assists users in making informed decisions based on their preferred model's outputs. Whether you're inclined towards the intricate patterns captured by LSTM or the interpretability offered by Meta Prophet, this web application provides a practical means to assess their respective strengths and weaknesses in the context of stock price prediction.

you can check out the application here

Meta Prophet:

Prophet is a forecasting tool developed by Facebook that is designed for time series data with strong seasonal patterns and holiday effects. It's particularly good at handling data with multiple seasonal components.

Pros:

• Handles missing data well.
• Provides interpretable components, such as trend, seasonality, and holidays.
• Easy to use and implement.

Cons:

• May not perform as well when dealing with highly volatile and noisy stock market data.
• Assumes that historical patterns and trends will continue into the future, which might not always hold true in stock markets.

LSTM Neural Network:

LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is well-suited for sequential data like time series. It has the ability to capture complex patterns and dependencies in the data.

Pros:

• Can capture intricate temporal patterns and relationships in stock data.
• Flexible and can handle various types of data, including noisy and non-linear data.
• Can be fine-tuned and optimized for specific datasets.

Cons:

• Requires more data preprocessing, feature engineering, and hyperparameter tuning.
• Prone to overfitting if not properly regularized.
• May not provide as interpretable insights as Prophet.

Which is Best for Stock Prediction?

The choice between Meta Prophet and LSTM for stock prediction hinges on the specific characteristics of your stock data and your prediction objectives. If your aim is to quickly grasp stock trends with an easily interpretable model, Meta Prophet is a suitable option. On the other hand, if your stock data exhibits intricate, non-linear patterns, LSTM neural networks excel in capturing such complexities. Additionally, consider your data size; LSTM models thrive with larger datasets, whereas Prophet can be more fitting when historical data is limited. Lastly, if you require clear explanations for predictions, especially for stakeholders, Prophet's interpretable components make it a preferable choice. Ultimately, the decision should align with your dataset's nature and the level of interpretability needed for your stock prediction task.