Team SillyCons Present,

Kash

Kash is a Stock Market Price Prediction for the next 10 days using machine learning algorithms. We have used machine learning and deep learning algorithms. You would like to model stock prices correctly, so as a stock buyer you can reasonably decide when to buy stocks and when to sell them to make a profit. This is where time series modelling comes in. You need good machine learning models that can look at the history of a sequence of data and correctly predict what the future elements of the sequence are going to be.

Uncertainity in the Stock Market

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At the most fundamental level, supply and demand in the market determine stock price. Price times the number of shares outstanding (market capitalization) is the value of a company. Comparing just the share price of two companies is meaningless. Theoretically earnings are what affect investors' valuation of a company, but there are other indicators that investors use to predict stock price. Remember, it is investors' sentiments, attitudes, and expectations that ultimately affect stock prices. There are many theories that try to explain the way stock prices move the way they do. Unfortunately, there is no one theory that can explain everything.

Machine Learning Implementation

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Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The good thing about stock price history is that it’s basically a well labelled pre formed dataset. There is a service called AlphaVantage. They offer the daily price history of NASDAQ stocks for the past 20 years. This includes the open, high, low, close and volume of trades for each day, from today all the way back up to 1999.

Stock Prices Prediction Using ML

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We will implement a mix of machine learning algorithms to predict the future stock price of this company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like Auto ARIMA and LSTM. The core idea behind this article is to showcase how these algorithms are implemented. We will briefly describe the technique and provide relevant links to brush up on the concepts as and when necessary.

Auto ARIMA

Introduction ARIMA is a very popular statistical method for time series forecasting. ARIMA models take into account the past values to predict the future values. There are three important parameters in ARIMA: p (past values used for forecasting the next value) q (past forecast errors used to predict the future values) d (order of differencing) Parameter tuning for ARIMA consumes a lot of time. So we will use auto ARIMA which automatically selects the best combination of (p,q,d) that provides the least error.

Long Short Term Memory (LSTM)

Introduction LSTMs are widely used for sequence prediction problems and have proven to be extremely effective. The reason they work so well is because LSTM is able to store past information that is important, and forget the information that is not. LSTM has three gates: The input gate: The input gate adds information to the cell state The forget gate: It removes the information that is no longer required by the model The output gate: Output Gate at LSTM selects the information to be shown as output.