FAQ

From Raw Data to Ready-to-use Information

Cleaning: The First Filter

When data is initially harvested, it often comes with a share of inaccuracies—missing values, duplicates, inconsistent entries, and even errors. These inaccuracies left untreated could muddle the learning process, leading the AI to draw faulty conclusions and its predictions astray.

Data cleaning thus serves as an essential first step towards ensuring that only accurate and complete data enters the AI system. Cleaning involves identifying and rectifying or removing any dubious data entries. Making sure no incorrect or misleading data slips through this sieve is crucial to maintain the predictive accuracy of EASY Quantum AI.

Normalization: Maintaining a Balanced Diet

Each type of data collected for EASY Quantum AI—be it price, volume, or trading indicators—comes in different scales and ranges. Feeding data with widely varying scales to an AI model is akin to feeding it with an unbalanced diet—it skews the model’s focus towards variables with larger scales.

This is why normalization is an indispensable part of pre-processing, as it rescales all data to a standard range—typically between 0 and 1. By bringing all these data under a uniform scale, EASY Quantum AI can better gauge the relative importance of each variable, leading to a balanced and accurate learning process.

Transformation: Getting Data in Shape

After cleaning and normalization, the various data must be transformed into a format that feeds seamlessly to the AI model. Raw data is usually unstructured and comes in disparate, incompatible formats that the AI cannot easily use.

Data transformation thus reshapes these disparate data blocks into a coherent structure. For EASY Quantum AI, this could mean turning data into numerical values that its algorithms can process or reshaping the data into vectors, matrices, or tensors that can be fed into its quantum computing core.

Why Pre-processing?

The cruciality of pre-processing in AI systems like EASY Quantum AI cannot be overstated. Only through meticulous cleaning, normalization, and transformation can raw data be rendered into a nutrient-rich form suitable for AI consumption. By these processes, EASY Quantum AI starts its journey with high-quality inputs, setting the stage for equally high-quality outputs in the form of accurate, reliable market forecasts. Thus, every step from raw data to ready-to-use information counts, setting the groundwork for intelligent trading in our increasingly digital financial markets.