### 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 2: Tests of the time-series properties of the dataa

"... In PAGE 7: ...ppendix 1. The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Appendix 2. Table2 : Tests of the time-series properties of the data .... In PAGE 16: ... Both the ADF test and the modified Phillips-Perron test allow us to test formally the null hypothesis that a series is I(1) against the alternative that it is I(0). The results from the tests of the time-series properties of the data can be found in Table2 in Appendix 2. ADF critical values are generated to account for the finite-sample distribution of the series by performing Monte Carlo simulations with 5,000 replications for the level of inventories, the level of new orders, capacity utilization, the price of raw materials and the yield spread.... In PAGE 16: ... Evidence was found that capacity utilization contains a moving-average component, while the yield spread appears to follow an autoregressive moving- average process.9 Table2 (Appendix 2) indicates that both the ADF and the tests suggest that inventories, new orders, and raw material prices are non-stationary or I(1) processes in levels. The ADF test rejects the null hypothesis of a unit root in the level of the yield spread at conventional levels of significance and also provides evidence that capacity utilization is characterized as a stationary or I(0) process.... ..."

### Table 2: Characteristics of temperature time series.

2003

"... In PAGE 12: ... In Buildings B and E we used micro- dataloggers to record temperatures. Table2 shows the temperature data collection parameters. Table 2: Characteristics of temperature time series.... In PAGE 12: ... For buildings from Organization 1, we did not rely exclusively on the HOT and COLD labels. For some of the buildings, the number of hot and/or cold complaints during the temperature monitoring intervals shown in Table2 was low. In these cases we extrapolated beyond the temperature monitoring interval in both directions equally until we either observed at least five complaints of each type or until the extended interval was equal to twice the temperature monitoring interval.... ..."

### Table 1. The Time Series used in the experiments.

1992

"... In PAGE 4: ...f some kind of transformation in the original data (e.g., logarithmic variation). *** insert Table1 around here *** *** insert Figure 2 around here *** To the experiments carried out in this work, a set of eight series were selected (Table 1 and Figure 2), ranging from nancial markets to natural processes (Box and Jenkins, 1976; Makridakis et al., 1998; Hyn- dman, 2003).... In PAGE 4: ...f some kind of transformation in the original data (e.g., logarithmic variation). *** insert Table 1 around here *** *** insert Figure 2 around here *** To the experiments carried out in this work, a set of eight series were selected ( Table1 and Figure 2), ranging from nancial markets to natural processes (Box and Jenkins, 1976; Makridakis et al., 1998; Hyn- dman, 2003).... In PAGE 7: ..., 1999). The above heuristics were tested in all series of Table1 using both EAs (AR and ARMA). The results of the last three columns are given in terms of the mean of the thirty runs, being the 95% con dence... In PAGE 21: ... Figure 2. The eight TSs of Table1 (passengers, paper, deaths, maxtemp, chemical, prices, sunspots and kobe) in a temporal perspective. Figure 3.... ..."

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### Table 1: Arti cial time series data without outliers.

"... In PAGE 3: ... Consider the outlier-contaminated time series shown in Figure 1. The outlier-free data consist of a random realization of n = 50 observations given in Table1 and generated from the AR(3) model, xt = 8 gt; lt; gt; : at t = 1, 2, 3 2:1xt?1 ? 1:46xt?2 + 0:336xt?3 + at t = 4; : : : ; 50; where fatg is a sequence of independent and identically distributed Gaussian variates with mean zero and variance 2 a = 1: The roots of the autoregressive polynomial are 0.... ..."

### Table 1. Various Data Sizes and SNR for Mackey Glass Time Series

"... In PAGE 5: ... A test set is required only when the early stopping criteria approach is considered. The test set data is taken from the training set as given in Table1 . Thus, the test set and the resulting training set together form the entire training data set given in Table 1.... ..."

### Table 1: Examples of time series clinical studies Reference Treatment /

"... In PAGE 1: ... For example, the Inflammation and the Host Response to Injury research program [3], a consortium of several leading research hospitals, stud- ies the response of over a hundred trauma and burn patients using time series expression data. Table1 lists a number of other examples of such studies. Time series expression experiments present a number of computational prob- lems [4].... In PAGE 2: ... Table1 . Examples of a number of time series clinical studies.... In PAGE 3: ... Most previous papers describing such data have relied on simple techniques such as averaging. See Table1 for some examples. As mentioned above, there have been a number of methods suggested for aligning two time series expression datasets.... In PAGE 11: ... These results are in good agreement with the (anecdotal) observations in the original paper mentioned above. As mentioned before ( Table1 ), previous attempts to combine patient expres- sion data used the average expression value for each time point. To compare our results with these methods we used k-means to cluster the average and median expression values from all six patients.... ..."

### Table 1: Time series used for energy preservation tests

2003

"... In PAGE 10: ... Time series with less than 10 samples extracted by this procedure were discarded. In Table1 all data sets used are listed with their original size and the number of samples of length 1024 that were created from them. Additional tests were made in order to evaluate the proposed method for clustering and classi cation with rules.... In PAGE 30: ...4 0.2 Table1 0: Additional energy by optimal method with Haar DWT... In PAGE 31: ...3 0.2 Table1 1: Additional energy by optimal method with DFT... ..."

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### Table 2: Promising methods for time-series data

2005

"... In PAGE 14: ... Finally, because uncertainty increases over time and seasonal influences might change, increased damping might improve accuracy for longer time horizons. Summary of promising methods for time-series data Table2 summarizes the gains for the promising methods for time series. The gains apply only for the conditions stated.... ..."

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