Objectives: Taiwan had its longest recorded cold wave during the 2016 Chinese New Year holiday; a surge in influenza cases followed. To assess the effect of a cold wave on influenza activity, we used a time series model to analyze the relationship between low ambient temperatures and influenza-related emergency visits. Methods: We obtained weekly data on the proportion of influenza-like illnesses (ILIs) among emergency room visits in Taipei from January 2007 to December 2016. Data were from the Real-time Outbreak and Disease Surveillance of the Taiwan Centers for Disease Control. Ambient temperature data were from the Taiwan Central Weather Bureau. We used an autoregressive integrated moving average (ARIMA) model to analyze the association between low temperatures and ILIs. The validity of predictive models was tested against January 2017 to December 2017 data as external validation. Results: The time series of ILI-related emergency visits in Taipei was consistent with the ARIMA (1, 1) model at an autoregression coefficient of 0.92 (p ＜ 0.001). ARIMA analysis indicated that the lowest temperature in the previous week (coefficient: -0.07, p = 0.01) and the number of holiday days in the week (coefficient: 0.2, p ＜ 0.001) were significant predictors of the proportion of ILI-related emergency visits. External validation demonstrated that the time series model with temperature and holiday covariates accurately predicted the timing of the ILI surge in the 2017 winter vacation, with an R^2 of 85% for the overall fit in 2017. Conclusions: Cold waves are a predictor of increased ILI-related emergency visits. Thus, influenza preparedness measures should incorporate ambient temperature forecasts.