### Table 10. MSE obtained from applying each forecasting technique to the Nut Core time series

"... In PAGE 14: ... Using the results from the confidence interval, it is a sensible choice to try the first three techniques and compare which one does a better forecasting job. Table10 shows the results in terms of the real MSE, where the best three predicted techniques matched the best three techniques. ... ..."

### Table 5: Evolutionary forecasting models. Series STW

"... In PAGE 5: ... 1 2 13 12 11 8 3 Figure 3: The best model for the sunspots series. Table5 shows the best models achieved by the GEA, for all series of Table 1. As an example, Figure 3 plots the best ANN topology for the sunspots series.... ..."

### Table 3 The best ANN structure for nonlinear time series

1999

"... In PAGE 12: ... The major di!erence among di!erent series is in the selected number of input nodes and/or hidden nodes required for model building and forecasting. Table3 gives the overall result in terms of the selected network structure for each time-series. It also lists the major characteristic components of the eight nonlinear series.... ..."

### Table 1: Time Series data

"... In PAGE 2: ...pproaches (e.g., least squares methods). To the experiments carried out in this work, a set of ten series was selected ( Table1 ), ranging from financial mar- kets to natural processes [3][14][10] (Figure 4). The last two series were artificially created, using the chaotic formulas: DCD8 BP CPDCD8A0BDB4BD A0 DCD8A0BDB5BN DCBC BP BCBMBEBN CP BP BG for the quadratic series [15]; and DCD8 BP BD A0 CPDCBE D8A0BD B7 CQDCD8A0BE, CP BP BDBMBG, CQ BP BCBMBF, DCBC BP BCBMBDBD for the henon one [2].... In PAGE 3: ... - the use of decomposable information; i.e., AF CBCCCF BPBO BDBN C3BN C3 B7BD BQ if the series is seasonal (period C3) and trended; AF CBCCCF BPBO BDBN C3 BQ if the series is seasonal ; and AF CBCCCF BPBO BD BQ and CBCCCF BPBO BDBN BE BQ if the series trended. Several FNNs, with a number of hidden nodes (D2CW) rang- ing from 0 to 13, were used to explore all sliding windows for each TS of Table1 . Each model was trained with 90% of the series elements, being the rest 10% used for the forecasts.... In PAGE 5: ... 1 2 13 12 11 8 3 Figure 3: The best model for the sunspots series. Table 5 shows the best models achieved by the GEA, for all series of Table1 . As an example, Figure 3 plots the best ANN topology for the sunspots series.... ..."

### Table 1: Time Series data

2001

"... In PAGE 2: ...pproaches (e.g., least squares methods). To the experiments carried out in this work, a set of ten series was selected ( Table1 ), ranging from financial mar- kets to natural processes [3][14][10] (Figure 4). The last two series were artificially created, using the chaotic formulas: a0 a1 a7 a18a17a33a0 a1 a49 a22 a5a20a19 a10 a0 a1 a49 a22 a7a9a21 a0 a23a22 a7 a18a24a23a25 a26a27a21a28a17 a7 a18a29 for the quadratic series [15]; and a0 a1 a7 a30a19 a10 a31a17a33a0a2a25 a1 a49 a22 a33a32a35a34 a0 a1 a49 a25 , a17 a7 a36a19a37a25 a29 , a34 a7 a38a24a27a25 a39 , a0 a23a22 a7 a40a24a23a25a41a19a37a19 for the henon one [2].... In PAGE 3: ... - the use of decomposable information; i.e., a41 a11 a1a0a3a2 a7 a5a4a4a19 a21 a25a42 a21 a43a42 a32 a19 a8 if the series is seasonal (period a42 ) and trended; a41 a11 a1a0a3a2 a7 a5a4a4a19 a21 a25a42 a8 if the series is seasonal ; and a41 a11 a1a0a3a2 a7 a5a4 a19 a8 and a11 a1a0a3a2 a7 a5a4 a19 a21 a26 a8 if the series trended. Several FNNs, with a number of hidden nodes (a10a44a38 ) rang- ing from 0 to 13, were used to explore all sliding windows for each TS of Table1 . Each model was trained with 90% of the series elements, being the rest 10% used for the forecasts.... In PAGE 5: ... 1 2 13 12 11 8 3 Figure 3: The best model for the sunspots series. Table 5 shows the best models achieved by the GEA, for all series of Table1 . As an example, Figure 3 plots the best ANN topology for the sunspots series.... ..."

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### Table 1. The best neural forecasting models

"... In PAGE 6: ... For these links, the tested windows were f1g, f1,2,3,4,5,6g and f1,2,3,144,145g(10 minute data) and f1g, f1,24,25g, f1,168,169g (1 hour scale). The forecasting neural models appear in Table1 . Interestingly, the multivariate neighborhood heuristic is the best option to forecast 11 (10 minute series) and 10 (hourly data) of the 18 links.... ..."

### Table 5. The best performances of PMRS and Neural Networks forecasting 10% of total data _______________________________________________________ Series

"... In PAGE 14: ... We next compare the RMSE, MAPE, GRMSE and GMRAE error measures of the two methods in Table 5. In Table5 , the PMRS algorithm and Neural Networks perform significantly well. We compare the best PMRS and neural network models.... ..."

### Table 6: Aggregate time-series: 1973-1990

### Table 1. Video Tutorial Titles

"... In PAGE 6: ...integrated library instruction classes, workshops and orientations. Examples of current video tutorial topics (with times) are listed in Table1 and Figures 1 - 3. Table 1.... ..."

### Table 5: RMSPE for forecasting quarterly and annual growth rates ( 1yt and 4yt) for 1989.I{1990.IV.

"... In PAGE 19: ... The airline model appears not to give useful forecasts. Finally in Table5 we give the RMSPEs for forecasts of 1yt and 4yt, which may sometimes be of interest in practice. In the rst panel, concerning 1yt, we observe that even though the 1 transformation appears relevant for the alcohol, energy and clothing series, the corresponding forecast are outperformed by PAR models (3 times) and airline models (once).... In PAGE 19: ... For total consumption we notice that the HEGY-AR model is best for 4- and 8-step ahead forecasts. From the second panel of Table5 , dealing with forecasts for the annual growth rates, we observe that the PAR model beats alternative models in 4 of the 8 cases. In sum, it seems that a carefully constructed PAR model, when proper account is taken of unit roots and deterministic terms, oftentimes yields better forecasts compared to those generated from HEGY-AR and airline models.... ..."

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