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Web app to predict closing stock prices in real time using Facebook's Prophet time series algorithm with a multi-variate, single-step time series forecasting strategy.
Web app to predict closing stock prices in real time using Facebook's Prophet time series algorithm with a multi-variate, single-step time series forecasting strategy.
Forecast Apple stock prices using Python, machine learning, and time series analysis. Compare performance of four models for comprehensive analysis and prediction.
A comprehensive dataset for stock movement prediction from tweets and historical stock prices.
This makes them extremely useful for predicting stock prices. This TensorFlow implementation of an LSTM neural network can be used for time series forecasting. Successful prediction of a stock's future price can yield significant profits for investors.
Stock Price Prediction : Stock (also known as equity) is a security that represents the ownership of a fraction of a corporation. This entitles the owner of the stock to a proportion of the corporation's assets and profits equal to how much stock they own.
Attempt to forecast future stock prices of any stock irrespective of country, market, currency traded using historical prices of similar stocks.
This repository focuses on building Time Series Model (Recurrent Neural Network- LSTM) to predict the stock price of the Apple.Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems that involves time series related events
The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression.
We can predict stock prices by parsing a date integer. For instance, we want to predict the price stock for tomorrow (considering we downloaded dataset today), we can excecute this line of code: reg.predict(np.array([[int(len(dates)+1)]]))