### Table 2 Computation time versus computer characteristics for the studied configuration Computer Type specifications Simulation time for 1 shot*

2001

"... In PAGE 12: ... For instance, as a compromise between complexity and speed of the delivery, in this study we had to step back from a preferred 12 tank-in-series plug-flow system to a 3 tank-in-series complete-mix configuration. Table2 summarises the calculation time required to run a 1-year shot on three different PCs... ..."

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### 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 II Hydrological Time Series.

1995

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### Table 13. The impact of the new ESA on the new time-series

1999

### Table 1. Examples of gene expression time series published in literature including unevenly sampled time series. Non-time series data points (e.g. mutants) published with the studies are not described

in warping

"... In PAGE 1: ...ner et al., 2000; Golub et al., 1999) is now commonplace. An important area of application of these techniques is the study of biological processes that develop over time by collecting RNA expression data at selected time points and analyzing them to identify distinct cycles or waves of expression (see Table1 ). Progress in the development of high throughput protein level assays (Gygi et al.... In PAGE 2: ... These conditions are easily met when sampling speech data through appropriate electronics and data processing, but not for RNA expression level data where collection of data at a time point involves laborious and costly steps. Examples of unevenly and sparsely sampled RNA expression time series are common in the literature (see Table1 ), and this will surely be true of protein time series as well. As a result, time warping algo- rithms developed for speech recognition cannot generally be directly applied to typical expression level time series.... ..."

### Table 6. Classification of Cross-Section Data by Number of Time Series Observations.

"... In PAGE 6: ...43 countries, with mean 55.3 and median 50.5. There are 29 data sets using time series observations in Table6 , with range 1 to 23, mean 5.3, median 3, and mode 1.... ..."

Cited by 4

### Table 1. Parameters of synthetic time series

1999

"... In PAGE 9: ...Table 1. Parameters of synthetic time series The basic parameters used to generate the synthetic databases are listed in Table1 . The parameters of LENGTHS (thelength of time series) and p (a period) are independently chosen.... ..."

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### Table 1. Parameters of synthetic time series

1999

"... In PAGE 9: ...Table 1. Parameters of synthetic time series The basic parameters used to generate the synthetic databases are listed in Table1 . The parameters of LENGTHS (the length of time series) and p (a period) are independently chosen.... ..."

Cited by 82