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Ref. | Forecasted variable(s) | Forecasting method(s) | Decomposition method(s) | Decomposition component(s) |
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[6] | Monthly load | ARIMA | X12 | Trend, seasonal, and irregular components. |
[7] | Daily and weekly load | NN | Wavelet transform | Trend load series under different frequency bands and the detailed load series. |
[8] | Monthly load | Hybrid method combining ARIMA, support vector machine (SVM), and Holt-Winters | Seasonal adjustments and H-P filter | Trend, seasonal, cyclic, and irregular components. |
[9] | Fault line(s) | Intrinsic mode function (IMF) | Empirical mode decomposition (EMD) | Zero sequence current at different frequencies. |
[10] | Monthly load | NN | Moving regression and smoothing spline decomposition models | Trend and fluctuation series. |
[11] | Monthly load | SARIMA | Multiplicative decomposition | Trend and seasonal components. |
[12] | Monthly load | ARIMA | X12 | Trend, seasonal, and random components. |
[13] | Monthly number of a software bug | Hybrid method combining ARIMA, X12, and polynomial regression | X12 | Seasonal and cyclic components. |
[14] | Monthly load | Hybrid method combining ARIMA and vector error correction (VEC) | X12 | Trends of load and economy, seasonality, holiday, and irregular components. |
[15] | Mean flying hours between failures for aircraft | Hybrid method combining grey box, back propagation NN (BPNN), and SVM. | STL | Long-term trend and seasonal components. |
[16] | Future geospatial incidence levels | Kernel density estimation with dynamic kernel bandwidth | STL | Annual, seasonal, weekend effect, and random components. |
[17] | Bids for amazon EC2 spot instances | Benchmarked time series forecasting methods such as naïve, ARIMA, and ETS | STL | Spike and seasonal components. |
[18] | Daily and weekly load | Hybrid method combining Holt-Winters and SVR | STL | Base component and weather sensitive component. |
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