A Novel Approach for Seasonality and Trend Detection using Fast Fourier Transform in Box-Jenkins Algorithm
Bibliographic record
Abstract
Forecasting is the first step to deal with the new generation of renewable energy systems. The accuracy of the forecasting techniques is very important. Time series technique is one of the powerful tools used for forecasting, but it works well with stationary data. In addition, non-stationary time series can cause unexpected behaviors or create a non-existing relationship between two variables. This work was motivated by the need of detecting the seasonality and trend for a given data. The trend and seasonality components are very important in dealing with forecasting. Based on the trend and seasonality we could use clustered regions and feed the clustered regions to a forecasting technique like ANN, WNN or Kalman Filtering. Then aggregating the forecasted data back again for a better performance. In this paper we use Fast Fourier Transform (FFT) in Box-Jenkins approach instead of Autocorrelation Function (ACF) for the seasonality and trend detection. The present study is validated using visual inspection, statistical tests and time series decomposition in identifying the trend and the seasonality by applying them to wind speed time series. The results of FFT technique and ACF are compared to the results of the most well-known techniques. The results show that some methods have minor limitations in determining either the trend or the seasonality compared to FFT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".