Forecasting through time domain analysis

1. A computer implemented method using a computing device with a processor and memory having instructions stored thereon which are executed by the processor to perform a method, comprising:

receiving, via a communication link, multiple cycle lengths from a combination of cycles of seasonal effects that are identified in time series data;

using a time domain analysis of the time series data to build a model; and

using the model to predict future events or future data points.

2. The method of claim 1 further comprising:

building an initial model from training sample data;

calibrating the model to include the multiple cycle lengths of seasonal effect.

3. The method of claim 1 further comprising, dividing the time series data into a first part for estimating the model and a second part for verifying the model.

4. The method of claim 1 further comprising, continuing to identify multiple cycle lengths of seasonal effects in the time series data until a final cycle length is deemed not relevant or reaches a user prescribed level.

5. The method of claim 1 further comprising, aggregating the multiple cycle lengths of seasonal effect.

6. The method of claim 1 further comprising, subtracting or dividing aggregated cycle data from the time series data to remove an effect of an identified cycle to confirm a next cycle.

7. The method of claim 1 further comprising, using the model for one of forecasting future events, performing data analysis, and analyzing quality control in a system.

8. A non-transitory computer readable medium having instructions stored thereon which are executed by a processor to perform a method, comprising:

identifying a combination of cycles in temporal data through a time domain analysis;

building a model from the identified cycles in the temporal data; and

using the model to predict future events or future data points.

9. The computer readable medium of claim 8 further comprising, performing an outlier analysis to identify deviations in the temporal data.

10. The computer readable medium of claim 8 further comprising, performing a stationary test on the temporal data.

11. The computer readable medium of claim 8 further comprising, replacing outliers discovered in the temporal data with a mean value.

12. The computer readable medium of claim 8 further comprising, computing a sample autocorrelation function to determine correlations in the temporal data at different points in time.

13. The computer readable medium of claim 8 further comprising, identifying cycle lengths of seasonal effect in the temporal data.

14. The computer readable medium of claim 8 further comprising, determining if the temporal data is non-stationary and has a trend.

15. The computer readable medium of claim 8 further comprising, using the model to model sales data to perform one of identifying cyclic effects in the sales data, identifying quarterly sales effect, or filtering the sales data.

16. A computer, comprising:

a processor;

a memory coupled to the processor; and

computer executable instructions stored on the memory and executed by the processor to:

receive plural cycle lengths from a combination of cycles of seasonal effects that are identified in time series data;

build a model using a time domain analysis of the time series data; and

use the model to predict future events or future data points.

17. The computer of claim 16 , wherein the processor further executes the instructions to:

compute an autocorrelation function for the time series data; and

determine if the autocorrelation function follows a normal distribution.

18. The computer of claim 16 , wherein the processor further executes the instructions to determine if the time series data is a random series.

19. The computer of claim 16 , wherein the processor further executes the instructions to determine if the time series data alternates with successive observations on different sides of a mean.

20. The computer of claim 16 , wherein the processor further executes the instructions to use the model to model sales data to perform one of identifying cyclic effects in the sales data, identifying quarterly sales effect, or filtering the sales data.