autoregressive time-series models
Sensitivity to Prior Specification in Bayesian Identification of Autoregressive Time Series Models
... real-world time series datasets to illustrate the use of the KL divergence to measure the distance between the posteriors and show the impact of this distance in the model ...the autoregressive ...
15
Likelihood Inference for Generalized Integer Autoregressive Time Series Models
... Received: 17 May 2019; Accepted: 2 October 2019; Published: 11 October 2019 Abstract: For modeling count time series data, one class of models is generalized integer autoregressive of order p ...
13
Nonlinear exponential autoregressive time series models with conditional heteroskedastic errors with applications to economics and finance
... some time series exhibit asymmetries which could be better explained by models that have both a nonlinear conditional mean and a changing conditional variance, there has not been much work on ...
391
Seasonal Interval Time Series Models in Comparative Study of Industrial Forecasting
... seasonal time series models and they have used hybrid models or combined several models in order to improve the accuracy of ...seasonal autoregressive integrated moving average ...
8
MODEL FOR MINIMUM AND MAXIMUM TEMPERATURE OF THE UPPER EAST REGION OF GHANA
... complex autoregressive model for forecasting monthly temperature ...complex autoregressive model in this work shows that using a complex number to fit a meteorological element field and predicting with the ...
11
Optimal Forecasting of Noncausal Autoregressive Time Series
... nonlinear models, we use the model estimated with actual data as the data generating process ...s) models for the demeaned seasonally adjusted an- nualized quarterly ...in‡ation series based on the ...
31
The Autoregressive metric for comparing time series models
... It is worth considering that other criteria are based on AR coefficients. In speech recognition analyses, the AR coefficients denoted as LPC (=Linear Pre- dictor Coding) were used in order to synthesize the voice signals ...
22
A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns
... topic models com- bined with time series regression analy- sis (autoregressive distributed-lag models) to gain insights about the language dy- namics in the political ...
10
Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA)
... on time series models, we analyzed and compared the Artificial Neural Networks (ANNs) and the Autoregressive Moving Averages (ARMA) in forecasting the weekly amounts of solid waste generated ...
13
Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach
... economic time series estimation and prediction, the most common models which were typically and frequently employed are autoregressive conditional heteroskedasticity or so-called the ARCH ...
19
Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review
... of time series are often taken to have been recorded at time intervals of one length when in fact they were recorded at time intervals of other, not necessarily regular, length is an important ...
79
UNIVARIATE TIME SERIES FORECASTING
... of time-series ...of models, namely autoregressive (AR), moving average (MA), and ARMA ...described models and methods are applied by the time- series of the Austrian ...
46
Econometric Analysis with Vector Autoregressive Models
... Vector autoregressive (VAR) models have a long tradition as tools for multiple time series analysis ...linear models, they are relatively easy to work with both in theory and ...VAR ...
60
Time series modelling and forecasting of Sarawak black pepper price
... up time series modelling and forecasting of the Sarawak black pepper ...that Autoregressive Moving Average (ARMA) time series models fit the price series well and they ...
17
Modeling and Forecasting of Carbon Dioxide Emissions in Bangladesh Using Autoregressive Integrated Moving Average (ARIMA) Models
... non-stationary time series analysis. In contrast to the regression models, the ARIMA model allows time se- ries to be explained by its past or lagged values and stochastic error ...The ...
8
Evaluating the Forecast Performance of Autoregressive Conditional Heteroscedasticity (ARCH) Family Models
... Generalized Autoregressive conditional heteroscedasticity models were propounded by [7] and ...financial time series model and has inspired dozens of more sophisticated ...financial ...
7
Prediction Of Fagm(1,1) Model Based On Cotes Formula In China's Local Fiscal Expenditure
... the autoregressive single-moving average model to predict and analyze fiscal expenditures; Sun et ...used time-series data VAR model to predict the trend of China's fiscal expenditure scale change; ...
7
Forecasting Inflation: Autoregressive Integrated Moving Average Model
... based models forecast better results than the univariate time series ...outlined Autoregressive integrated moving average (ARIMA) models to forecasting inflation in ...vector ...
10
ARIMA MODELLING OF FOOD INFLATION RATE IN NIGERIA
... a time series model to the consumer price index (CPI) in Nigeria’s Inflation rate between 1980 and 2010 and provided five years forecast for the expected CPI in ...Box-Jenkins Autoregressive ...
11
FORECASTING MANILA SOUTH HARBOR MEAN SEA LEVEL USING SEASONAL ARIMA MODELS
... Seasonal Autoregressive Integrated Moving Average (SARIMA) models that fits the given time series composed of the mean sea level of the Manila South Harbor from 2008 to 2014 measured in ...
7