Authoritative Financial Forecasting
AI Certain enables accurate financial markets forecasting with deep learning neural networks.
Optimize the future by learning from the past
AI Certain's technology creates an authoritative market forecast for equities and commodities. Our deep learning neural networks ingest millions of data elements, across multiple data sources, perform quadrillions of computations, creating an accurate machine forecasting model.
Financial strategies are improved with statistically accurate forecasting.
Financial Forecasting with AI Certain
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AI Certain's neural networks generate accurate probabilities for equity and commodities movement across numerous future time frames. Our probability models enable more accurate buy / sell signals and improved options strategies.
AI Certain's "artificial minds" find complex repeatable patterns in millions of data points and then future test thousands of scenarios. The output? Authoritative Forecasting. |
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What makes AI Certain's Technology Different?
AI Certain technology removes distribution bias
Traditional approaches to machine/deep learning predictions/forecasting typically predict an outcome that assumes a normalized distribution. This assumption implies that for a predicted value, nearby values are also "likely" and far away values are much "less likely". In the real world, this assumption is not correct for many problem domains because the likely outcomes may be biased toward higher values, lower values, or on the extremes. For example, a stock may be highly volatile ahead of an earnings announcement and have a likelihood of an increase in value and a nearly as likely decrease in value but a low likelihood of staying near its current value.
Traditional approaches to machine/deep learning predictions/forecasting typically predict an outcome that assumes a normalized distribution. This assumption implies that for a predicted value, nearby values are also "likely" and far away values are much "less likely". In the real world, this assumption is not correct for many problem domains because the likely outcomes may be biased toward higher values, lower values, or on the extremes. For example, a stock may be highly volatile ahead of an earnings announcement and have a likelihood of an increase in value and a nearly as likely decrease in value but a low likelihood of staying near its current value.
AI Certain technology processes N-dimensional data
AI Certain combines multidimensional data for analysis. We apply this method to look at historical performance data across correlated equities. As an example, equities movement is not just based on an individual equity, but on groups of equities. Each of those equities has its own data that includes items like news, options, tick data, fundamentals, and insider transaction information. It is not sufficient to look at an equity as a stand-alone entity against this data. AI Certain's technology can infer relationships across this entire set of data to create a more accurate statistical forecast.
AI Certain combines multidimensional data for analysis. We apply this method to look at historical performance data across correlated equities. As an example, equities movement is not just based on an individual equity, but on groups of equities. Each of those equities has its own data that includes items like news, options, tick data, fundamentals, and insider transaction information. It is not sufficient to look at an equity as a stand-alone entity against this data. AI Certain's technology can infer relationships across this entire set of data to create a more accurate statistical forecast.