Table 1 Estimating museum visitor numbers using Google search data. We aim to generate rapid estimates of the monthly number of visitors at a given museum using adaptive nowcasting [19] with minimum delay as soon as each month is over. We build adaptive nowcasting models using autoregressive integrated moving average (ARIMA) models, and neural network autoregression (NNAR) models. For both families of time series models, we construct a baseline model using historical visitor numbers only. We also construct a Google Trends model, in which data on the volume of Google searches for a museum is used as an additional predictor. All models reported here are trained using the previous 60 months of data. We generate monthly estimates for the period between January 2010 and December 2018, and report the mean absolute percentage error (MAPE). For each comparison between a baseline model and an equivalent Google Trends model, the smallest MAPE is highlighted in bold. We observe that nearly all models which include data derived from Google Trends exhibit a smaller MAPE than their counterpart baseline model based on historical visitor numbers alone

7.79

\( \textbf{7}\textbf{.}\textbf{22} \)

9.07

\( \textbf{7}\textbf{.}\textbf{74} \)

11.55

\( \textbf{11}\textbf{.}\textbf{55} \)

13.76

\( \textbf{10}\textbf{.}\textbf{27} \)

12.49

\( \textbf{10}\textbf{.}\textbf{60} \)

14.43

\( \textbf{12}\textbf{.}\textbf{09} \)

16.84

\( \textbf{13}\textbf{.}\textbf{84} \)

17.80

\( \textbf{15}\textbf{.}\textbf{42} \)

\( \textbf{11}\textbf{.}\textbf{63} \)

12.24

12.89

\( \textbf{11}\textbf{.}\textbf{59} \)

11.03

\( \textbf{9}\textbf{.}\textbf{62} \)

14.98

\( \textbf{12}\textbf{.}\textbf{20} \)

11.17

\( \textbf{9}\textbf{.}\textbf{60} \)

13.46

\( \textbf{9}\textbf{.}\textbf{92} \)

7.53

\( \textbf{6}\textbf{.}\textbf{93} \)

9.83

\( \textbf{8}\textbf{.}\textbf{83} \)

16.86

\( \textbf{15}\textbf{.}\textbf{89} \)

18.41

\( \textbf{17}\textbf{.}\textbf{06} \)

7.63

\( \textbf{7}\textbf{.}\textbf{35} \)

10.13

\( \textbf{8}\textbf{.}\textbf{88} \)

21.46

\( \textbf{13}\textbf{.}\textbf{09} \)

24.11

\( \textbf{14}\textbf{.}\textbf{31} \)

12.11

\( \textbf{8}\textbf{.}\textbf{74} \)

13.81

\( \textbf{10}\textbf{.}\textbf{85} \)

15.15

\( \textbf{12}\textbf{.}\textbf{81} \)

17.87

\( \textbf{14}\textbf{.}\textbf{47} \)

58.14

\( \textbf{52}\textbf{.}\textbf{30} \)

61.13

\( \textbf{52}\textbf{.}\textbf{11} \)

6.75

\( \textbf{6}\textbf{.}\textbf{65} \)

7.45

\( \textbf{6}\textbf{.}\textbf{89} \)

12.64

\( \textbf{11}\textbf{.}\textbf{77} \)

14.34

\( \textbf{12}\textbf{.}\textbf{83} \)