nog stock prediction : The S&P 500 knocked off as much as 27% in the big COVID-19 crash. -

The S&P 500 knocked off as much as 27% in the big COVID-19 crash. It's since rallied to all-time highs above 3,600.

Time Series is being widely used in analytics & data science. This is specifically designed time series problem for you and the challenge is to forecast traffic.

Thanks for sharing valuable information. a worth reading blog. I try this algorithm for my example and it works excellent.

For our problem statement, we do not have a set of independent variables. We have only the dates instead. Let us use the date column to extract features like – day, month, year,  mon/fri etc. and then fit a linear regression model.

Predicting how the stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction – physical factors vs. physhological, rational and irrational behaviour, etc. All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy.

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Wow! The LSTM model can be tuned for various parameters such as changing the number of LSTM layers, adding dropout value or increasing the number of epochs. But are the predictions from LSTM enough to identify whether the stock price will increase or decrease? Certainly not!

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Can we use machine learning as a game changer in this domain? Using features like the latest announcements about an organization, their quarterly revenue results, etc., machine learning techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions.

IndexError: only integers, slices (`:`), ellipsis (`…`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

In the next section, we will look at two commonly used machine learning techniques – Linear Regression and kNN, and see how they perform on our stock market data.

I don’t think you understand. All the rows with zeroes are stored in the validation set, where it shouldn’t be seen at all. The last training data point is on March to 19th (I am using Google NASDAQ data), and the first few data points are actual stock values. How am I supposed to fill it with predicted values when I can’t make it predict? When I fill it with NaN’s, it just doesn’t predict.

Prophet (like most time series forecasting techniques) tries to capture the trend and seasonality from past data. This model usually performs well on time series datasets, but fails to live up to it’s reputation in this case.

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I have used 4 years data for training and 1 year for testing. Splitting at 987 distributes the data in required format.

We’ll dive into the implementation part of this article soon, but first it’s important to establish what we’re aiming to solve. Broadly, stock market analysis is divided into two parts – Fundamental Analysis and Technical Analysis.

The profit or loss calculation is usually determined by the closing price of a stock for the day, hence we will consider the closing price as the target variable. Let’s plot the target variable to understand how it’s shaping up in our data:

Nokia is one of the major players in the budding 5G market. The company supplies a range of products and services necessary for a 5G network, including chips, radio, cloud, automation, and security.

Linear regression is a simple technique and quite easy to interpret, but there are a few obvious disadvantages. One problem in using regression algorithms is that the model overfits to the date and month column. Instead of taking into account the previous values from the point of prediction, the model will consider the value from the same date a month ago, or the same date/month a year ago.

The command is only train[‘Date’].min(), train[‘Date’].max(), valid[‘Date’].min(), valid[‘Date’].max() , the timestamp is the result I got by running the above command.