A system and method provide spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the system comprising a data collection module, wherein the data collection module is configured to record one or more recordings. Further, the system includes a filter, wherein the filter is configured to clean the one or more recordings made by the data collection module. Furthermore, the system includes a time series historian configured to store the cleaned one or more recordings as a time series data set. In addition, the system includes a determination module, the determination module comprising one or more processors and a non-transitory memory containing instructions that, when executed by said one or more processors, cause said one or more processors to perform a set of steps.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the system comprising: a data collection module, wherein the data collection module is configured to record one or more recordings; a filter, wherein the filter is configured to clean the one or more recordings made by the data collection module; a time series historian configured to store the cleaned one or more recordings as a time series data set; and a determination module, wherein the determination module comprises: one or more processors; and a non-transitory memory containing instructions that, when executed by said one or more processors, cause said one or more processors to perform a set of steps comprising: subtracting the mean of the time series data set from each element of the time series data set for making the time series data set mean centric; detrending the mean centric time series data set; obtaining a power spectrum of the de-trended mean centric time series data set; selecting a set of frequencies from the power spectrum of the mean centric time series data set, wherein the selecting of the set of frequencies is done based on energy of the frequencies, the energy being the highest in the power spectrum; reconstructing the time series data set from selected set of frequencies; and determining the cycle of optimal periodicity from the reconstructed time series. 2. The system as claimed in claim 1, wherein the one or more processors are further configured to reconstruct the time series data set from the selected set of frequencies by applying inverse fast Fourier transform on the selected set of frequencies. 3. The system as claimed in claim 1, wherein the one or more processors obtain the power spectrum of the mean centric time series data sets by applying fast Fourier transform on the mean centric time series data set. 4. The system as claimed in claim 1, wherein the one or more processors determine the cycle of optimal periodicity using autocorrelation technique. 5. The system as claimed in claim 1, wherein the one or more processors are further configured to forecast a set of future points based on the determined optimal periodicity and reconstructed time series data set, wherein the forecasting is performed by replicating the determined optimal periodicity present in the reconstructed time series data set in the future horizon for obtaining a set of future points. 6. The system as claimed in claim 5, wherein the one or more processors are further configured to perform reverse differencing on the set of future points. 7. The system as claimed in claim 6, wherein the one or more processors are further configured to add the mean of the time series data set to the set of future points for obtaining the forecasted time series data set. 8. A method for spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the method comprising: subtracting a mean of the time series data set from each element of the time series data set for making the time series data set mean centric; performing first order differencing on the mean centric time series data set for detrending the mean centric time series data set; obtaining the power spectrum of the de-trended mean centric time series data set; selecting a set of frequencies from the power spectrum of the mean centric time series data set, wherein the selecting of the set of frequencies is done based on energy of the frequencies, the energy being the highest in the power spectrum; and determining the cycle of optimal periodicity from the selected set of frequencies. 9. The method as claimed in claim 8, further comprising reconstructing the time series data set from the selected set of frequencies by applying inverse fast Fourier transform on the selected set of frequencies. 10. The method as claimed in claim 8, further comprising forecasting a set of future points based on the determined optimal periodicity and the reconstructed time series data set, wherein the forecasting is performed by replicating the determined optimal periodicity present in the reconstructed time series data set in the future horizon for obtaining a set of future points. 11. The method as claimed in claim 10 further comprising, performing reverse differencing on the set of future points. 12. The method as claimed in claim 10 further comprising, adding the mean of the time series data set to the set of future points for obtaining the forecasted time series data set.