| dc.description.abstract | The current statistical analysis methods for time series are mainly based on autoregressive integrated moving average (ARIMA) and Fast Fourier Transform (FFT). Both the ARIMA and FFT assume that the time series data under study is stationary. However in practice, data may have different periods at different time. Therefore, this article considers the analysis of time series by using the statistical inference based on the Hilbert-Huang transform (HHT). The time series is decomposed by the HHT into several Intrinsic Mode Functions (IMFs), the goodness of fit test along with the Ljung-Box test or Durbin-Watson test are used to choose and combined several IMFs of high frequencies integrated to be the noise, and the other IMFs are integrated as the signal. Finally, we can build statistical inference for signal or nonparametric model based on bootstrap method. This article also discusses how to use the HHT to construct a regression model for the statistical relationship of two time series. This article uses the data of sea level recorded at the Zhuwei Fishing Harbor on Jan. 29th, 2008, to illustrate the application of HHT’s noise, and uses the transaction price of the early autumn varieties of cabbage from Taoyuan Agricultural products marketing from Jan. 2013 to Dec. 2017 to describe the variation of the transaction price over time. Finally, how the transaction weight and the transaction price is correlated is discussed. In terms of sea level data, the noise of HHT can detect the start time of meteorological tsunami more accurately than FFT. For the cabbage data, the HHT is more suitable than ARIMA or FFT to show the variation of the ratio of price and weight. Moreover, the HHT signal of the transaction weight provide a better prediction of the transaction price. To sum up, when the period of time series changes with time, applying the HHT statistical method established in this article can be more reasonably for describing the variation or trend of the time series. | en_US |