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as 06-11-2024 2:31pm EST

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Stocks

Consumer Discretionary

Diversified Commercial Services

Nasdaq

Forrester Research Inc is a United States-based company that provides independent research, data, and advisory services. It operates through the following segments: The Research segment develops and delivers research, connect, and analytics products; The consulting segment includes the revenues and the related costs of the company's consulting organization, and the Events segment is engaged in developing and hosting in-person and virtual events.

More About Our FORR ML Model...

What kind of parameters do you use to train FORR stocks ML model?

To train our stocks FORR ML model, we use historical data with over 25 parameters, including volume indicators, volatility indicators, momentum indicators, trend indicators, and other metrics.

How often do you update FORR ML model?

We update our ML model either weekly or biweekly, depending on the market capitalization of the stocks, using TensorFlow, typically over the weekend.

Why is the accuracy of your FORR model low?

Unfortunately, our current data provider offers limited historical data, which impacts the accuracy of our ML model. However, as we continue to gather more data over time and add more indicators, we expect the accuracy of our model to improve.

How can I provide and share more data with you to increase your ML model accuracy?

We would greatly appreciate your contribution. Please send an email to [email protected]

Do you offer an ML model for shorter time periods, such as 5 minutes or 15 minutes?

Yes, we offer it for strategy purposes only, with intervals: 1 minute, 2 minutes, 5 minutes, 15 minutes, and 30 minutes etc.

Can I rely on your ML model to make financial decisions regarding buying or selling stocks, or is it only intended for learning purposes?

Our ML model is primarily designed for educational purposes and is not intended to provide financial advice. We strongly recommend consulting with a financial advisor before making any buying or selling decisions.

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