Since previous studies have shown that confounding of the cold effect by air pollution is not very strong [ 6, 7] we preferred to use a complete time series over adjustment for it that would have meant a substantial loss of power.
The AMB approach requires the identification of an ARIMA model for the series at hand and in addition many real time series need pre-adjustment before ARIMA models can be assumed due the possible presence of outliers, calendar effects and missing observations.
To control for secular trends we used an interrupted-time series with adjustment for auto-correlation.
To control for secular trends we used an interrupted-time series with adjustment for autocorrelation using an autoregressive integrated moving average (ARIMA) model as described elsewhere [ 26].
Because the initial two time series resemble each other, the adjustment is kept small.
In seasonal adjustment a time series is considered as a juxtaposition of several components, the trend-cycle, and the seasonal and irregular components.
Thus the adjustments to the time series derived from the administrative sources based on using these probability weights should be considered as an aggregate adjustment to the time series.
The main idea of the proposed method is a combination of an off-line static strategy using time series prediction and an on-line dynamic adjustment using reinforcement learning.
Goodwin and Harper (2000) found that recent time series models examining price transmission consider asymmetric adjustments linear.
Especially, Goodwin and Harper (2000) point out that many recent time series models examining price transmission consider asymmetric adjustments assume that the price transmission process to be fundamentally linear.
