Power-Type Functions of Prediction Error of Sea Level Time Series
نویسندگان
چکیده
منابع مشابه
Power-Type Functions of Prediction Error of Sea Level Time Series
This paper gives the quantitative relationship between prediction error and given past sample size in our research of sea level time series. The present result exhibits that the prediction error of sea level time series in terms of given past sample size follows decayed power functions, providing a quantitative guideline for the quality control of sea level prediction.
متن کاملModeling and prediction of time-series of monthly copper prices
One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...
متن کاملTidal prediction using time series analysis of Buoy observations
Although tidal observations which are extracted from coastal tide gages, have higher accuracy due to their higher sampling rate, installing these types of gages can impose some spatial limitation since we cannot use every part of sea to install them. To solve this limitation, we can employ satellite altimetry observations. However, satellite altimetry observations have lower sampling rate. Acco...
متن کاملSome New Methods for Prediction of Time Series by Wavelets
Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...
متن کاملAccumulative prediction error and the selection of time series models
This article reviews the rationale for using accumulative one-step-ahead prediction error (APE) as a data-driven method for model selection. Theoretically, APE is closely related to Bayesian model selection and the method of minimum description length (MDL). The sole requirement for using APE is that the models under consideration are capable of generating a prediction for the next, unseen data...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Entropy
سال: 2015
ISSN: 1099-4300
DOI: 10.3390/e17074809