A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements, forecasting the hidden evidence into future forecasted cycle measurements, and computing a causation link based on the future forecasted cycle measurements.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A hidden evidence correlation and causation linking system, the system comprising: a correlation computing device configured to compute a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements; a forecasting device configured to forecast the hidden evidence into future forecasted cycle measurements; and a causation computing device configured to compute a causation link based on the future forecasted cycle measurements. 2. The system of claim 1, further comprising an extraction device configured to extract, for a given time domain, temporal relations with a topic based on the user input from a large corpus of data. 3. The system of claim 2, wherein the extraction device converts the temporal relations to a time versus frequency graph by using a Fast Fourier Transform (FFT). 4. The system of claim 2, further comprising an evidence finding device configured to find evidence based on the temporal relations from the extraction device. 5. The system of claim 2, further comprising an evidence finding device configured to find evidence based on the temporal relations from the extraction device, wherein after finding the evidence, the evidence finding device determines a principal time series component of the evidence. 6. The system of claim 5, further comprising a re-sampling device configured to re-sample the temporal relations found by the evidence finding device to find hidden evidence based on the principal time series component. 7. The system of claim 6, wherein the hidden evidence comprises a re-sampled piece of evidence from the principal time series component. 8. The system of claim 1, wherein the forecasted cycle measurements are transformed into an amplitude versus frequency histogram, and wherein each histogram is compared and the causation computing device computes a probability density function to produce a degree of causation between the hidden evidence. 9. The system of claim 1, further comprising a first filtering device configured to filter out false negatives of hidden evidence that are correlated to each other by the correlation computing device. 10. The system of claim 9, wherein the first filtering device includes a first answer key associated with a high correlation and a low correlation between the hidden evidence in which parameters are compared of the hidden evidence to calculate a correlation threshold value, and wherein the first filtering device determines if the correlations are the false negatives based on a threshold value compared to the correlation threshold value. 11. The system of claim 1, further comprising a filtering device configured to filter out false positives of the hidden evidence that is determined to have a causation to each other by the causation computing device. 12. The system of claim 11, wherein the filtering device includes a second answer key associated with a high correlation and a low correlation between the hidden evidence, and wherein based on a comparison with the second answer key, a causation score with training data is used to minimize false positives and includes semantic features. 13. The system of claim 1, further comprising a first filtering device configured to filter out false negatives of hidden evidence that are correlated to each other by the correlation computing device; and a second filtering device configured to filter out false positives of the hidden evidence that is determined to have a causation to each other by the causation computing device. 14. The system of claim 13, wherein the first filtering device includes a first answer key associated with a high correlation and a low correlation between the hidden evidence in which parameters are compared of the hidden evidence to calculate a correlation threshold value, wherein the first filtering device determines if the correlations comprise the false negatives based on a threshold value compared to the correlation threshold value, wherein the second filtering device includes a second answer key associated with a high correlation and a low correlation between the hidden evidence, and wherein based on a comparison with the second answer key, a causation score with training data is used to minimize false positives and includes semantic features. 15. The system of claim 5, further comprising a first filtering device configured to filter out false negatives of hidden evidence that are correlated to each other by the correlation computing device; and a second filtering device configured to filter out false positives of the hidden evidence that is determined to have a causation to each other by the causation computing device, wherein the first filtering device includes a first answer key associated with a high correlation and a low correlation between the hidden evidence in which parameters are compared of the hidden evidence to calculate a correlation threshold value, wherein the first filtering device determines if the correlations comprise the false negatives based on a threshold value compared to the correlation threshold value, wherein the second filtering device includes a second answer key associated with a high correlation and a low correlation between the hidden evidence, and wherein based on a comparison with the second answer key, a causation score with training data is used to minimize false positives and includes semantic features. 16. The system of claim 1, further comprising an extraction device configured to extract, for a given time domain, temporal relations with a topic based on the user input from a large corpus of data; an evidence finding device configured to find evidence based on the temporal relations from the extraction device; and a re-sampling device configured to re-sample the temporal relations found by the evidence finding device to find hidden evidence based on a principal time series component. 17. A non-transitory computer-readable recording medium recording a program for hidden evidence correlation and causation linking, the program causing a computer to perform: computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements; forecasting the hidden evidence into future forecasted cycle measurements; and computing a causation link based on the future forecasted cycle measurements. 18. The non-transitory computer-readable recording medium of claim 17, wherein the forecasted cycle measurements are transformed into an amplitude versus frequency histogram, and wherein each histogram is compared and the computing the causation link further applies a probability density function to produce a degree of causation between the hidden evidence. 19. A hidden evidence correlation and causation linking method, the method comprising: computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements; forecasting the hidden evidence into future forecasted cycle measurements; and computing a causation link based on the future forecasted cycle measurements. 20. The method of claim 19, wherein the forecasted cycle measurements are transformed into an amplitude versus frequency histogram, and wherein each histogram is compared and the computing the causation link further applies a probability density function to produce a degree of causation between the hidden evidence.