Case VII On the Forecasting of Freight Transportation by a New Model Fitting Procedure of Time Series

1993 ◽  
pp. 207-234
Keyword(s):  
Time Series ◽  
Model Fitting ◽  
Freight Transportation ◽  
New Model ◽  
Fitting Procedure

scholarly journals Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model

2017 ◽  
Vol 8 (3) ◽  
pp. 154
Author(s):  
Kaiying Sun
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Model Fitting ◽  
Garch Model ◽  
Information Criterion ◽  
Equity Returns ◽  
Series Data ◽  
Arima Models ◽  
Fitting Procedure

In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.

scholarly journals QUANTILE DOUBLE AUTOREGRESSION

2021 ◽  
pp. 1-47
Author(s):  
Qianqian Zhu ◽  
Guodong Li
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulation Studies ◽  
Finite Sample ◽  
Conditional Quantile ◽  
New Model ◽  
Conditional Quantiles ◽  
Financial Time ◽  
Portmanteau Tests ◽  
Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

The Modeling of Great Salt Lake Elevation Time Series Based on ARFIMA With Stable Innovations

2009 ◽  
Author(s):  
Hu Sheng ◽  
YangQuan Chen
Keyword(s):  
Time Series ◽  
Fractional Integral ◽  
Salt Lake ◽  
Moving Average ◽  
Great Salt Lake ◽  
Western Hemisphere ◽  
Conditional Heteroskedasticity ◽  
New Model ◽  
The People ◽  
Auto Regressive

Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourth-largest terminal lake in the world. The elevation of Great Salt Lake has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as FARIMA or ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity), have been built to characterize the variation of Great Salt Lake elevation, these models can not characterize it perfectly. Therefore, it became a key point to build a more appropriate model of GSL elevation time series. In this paper a new model based on fractional autoregressive integrated moving average (ARFIMA) with Stable innovations is applied to analyze the data and predict the future levels. From the analysis we can see that the new model can characterize GSL elevation time series more accurately. The new model will be beneficial to predict GSL elevation more precisely.

scholarly journals Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting

2019 ◽  
Vol 11 (21) ◽  
pp. 2558 ◽  
Author(s):  
Emily Myers ◽  
John Kerekes ◽  
Craig Daughtry ◽  
Andrew Russ
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Model Fitting ◽  
Series Data ◽  
Sigmoid Function ◽  
Absolute Deviation ◽  
Shape Model ◽  
Area Index ◽  
The Impact

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

Short-term Forecasting in Marine Fish Stocks

1981 ◽  
Vol 38 (10) ◽  
pp. 1247-1254 ◽  
Author(s):  
Max Stocker ◽  
Ray Hilborn
Keyword(s):  
Time Series ◽  
Marine Fish ◽  
Management Practice ◽  
Model Fitting ◽  
Production Model ◽  
Catch Per Unit Effort ◽  
Short Term ◽  
Fish Stocks ◽  
Production Models ◽  
The Difference

The predictive power of stock production models and simple time series methods was considered for five marine fish stocks. The distinction between model fitting and forecasting future short-term catch is discussed, as is the difference between techniques to forecast short-term yield, and techniques to calculate long-term management practice. Fox's procedure for fitting Pella and Tomlinson's stock production model, Schnute's method for fitting Schaefer's model, and Gulland's method are all considered. We found that all methods except that of Gulland work well for some stocks, and the relative performance of the methods depends upon the exploitation history of the stock. In several instances one of the best forecasts of next year's catch per unit effort (CPUE) was the previous year's CPUE, emphasizing the fact that a good forecasting technique may have no utility in determining management policies.Key words: production models, catch and effort, fisheries, management, catch forecasting, time series

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