Time-Series Models as Mixed Linear Models

2016 ◽  
pp. 189-200
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Time Series Models ◽  
Mixed Linear Models

scholarly journals Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming

2020 ◽  
Vol 15 (2) ◽  
pp. 47
Author(s):  
Arturo Rodríguez ◽  
Joaquín Trigueros
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Linear Models ◽  
Predictive Accuracy ◽  
Forecasting Model ◽  
Time Series Models ◽  
Arima Models ◽  
Quarterly Earnings ◽  
Earnings Per Share ◽  
Forecast Combinations

In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.

scholarly journals How well do linear and nonlinear time series models’ forecasts compete with international economic organizations?

2020 ◽  
Vol 12 (3) ◽  
pp. 23-70
Author(s):  
Tayyab Raza Fraz ◽  
Javed Iqbal ◽  
Mudassir Uddin
Keyword(s):  
International Economic ◽  
Time Series ◽  
Linear Models ◽  
Nonlinear Models ◽  
Nonlinear Time Series ◽  
Time Series Models ◽  
Macroeconomic Variables ◽  
The Past ◽  
Economic Organizations ◽  
Linear And Nonlinear

This paper evaluates the forecasting performance of linear and non-linear time series models of some macroeconomic variables viz a viz the forecasts outlook of these variables generated by professionals in international economic organizations i.e. the International Monetary Fund (IMF) and the Organization of Economic Cooperation and Development (OECD). Many time series and econometrics models are used to forecast financial and macroeconomic variables. The accuracy of such forecasts depends crucially on careful handling of nonlinearity present in the time series. The debate of forecasting ability of linear vs nonlinear models is far from settled. These models use the past patterns of the economic time series to infer the parameters of the underlying stochastic process and use them to make forecasts. In doing so these models use only the information contained in the past data. However the economists working in professional international economic organizations not only look at the past trends but use the condition of local and global economy prevailing at the time and expected future path of economies as well as their professional expertise and judgment to arrive at forecasts of macroeconomic variables. However the specific underlying models and methodology used by the economists generating these forecast is usually not communicated to the public. In comparison to the forecasts of these organizations the time series models are well developed and accessible to researchers working anywhere around the globe. Thus it is an interesting task to compare the foresting ability of linear and nonlinear time series models. This paper aims at comparing the forecasts from these models to assess how well they compete with forecasts generated from the professional economists employed by international economic organizations. The nonlinear models employed in this study are quite well known namely the Self Exciting Threshold Autoregressive (SETAR) model and the Markov Switching Autoregressive (MSAR) model. The linear models employed are the AR and ARMA models. The paper have used annual data of three macroeconomic time series variables GDP growth, consumer price inflation and exchange rate of G7 countries i.e. Canada, France, Germany, Italy, Japan, United Kingdom (UK) and United States of America (USA) as well as an emerging south Asian economy namely Pakistan. Three forecast accuracy criteria i.e. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are employed and the statistical significance of difference in forecasts is assessed using the Diebold-Mariono test. The results show that the forecasting ability of nonlinear Regime Switching models SETAR and MSAR is superior to the linear models. Further, although the point forecasts of linear and nonlinear models are not superior to that of economic organizations but in more than 60 percent of the cases considered the forecasting accuracy of two sets of forecast is not statistically significantly different.

scholarly journals Hybrid Time Series Models for Forecasting Maize Production in India

2021 ◽  
pp. 49-57
Author(s):  
Pramit Pandit ◽  
Bishvajit Bakshi ◽  
Varun Gangadhar
Keyword(s):  
Time Series ◽  
Model Building ◽  
Linear Models ◽  
Linear Time ◽  
Arima Model ◽  
Hybrid Models ◽  
Time Series Models ◽  
Real World Data ◽  
Maize Production ◽  
Non Linear

In spite of the immense success of different linear and non-linear time series models in their respective domains, real-world data are rarely pure linear or non-linear in nature. Hence, a hybrid modelling framework with the capability of handling both linear and non-linear patterns can substantially improve the forecasting accuracy. With this backdrop, an effort has been made in this investigation to evaluate the suitability of hybrid models in compassion to single linear or non-linear models for forecasting maize production in India. Data from 1949-50 to 2016-17 have been utilised for the model building purpose while retaining the data from 2017-18 to 2019-20 for the post-sample accuracy assessment. Outcomes emanated from this investigation clearly reveals that the ARIMA-NLSVR model has outperformed all other candidate models employed in this study. It is noteworthy to mention that both the hybrid models have performed better than their individual counterparts. The superior forecasting ability of both the non-linear models over the linear ARIMA model has also been evident.

Analysis and prediction of groundwater level time series with Autoregressive Linear Models

2016 ◽  
Vol 39 ◽  
pp. 109-112
Author(s):  
Mirko Ginocchi ◽  
Giovanni Franco Crosta ◽  
Marco Rotiroti ◽  
Tullia Bonomi
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Linear Models

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Recent Developments

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Inheritance and Correlation Analysis of Main Agronomic Traits in Mungbean (Vigna radiata L.) by Mixed Linear Models

2013 ◽  
Vol 38 (4) ◽  
pp. 624-631
Author(s):  
Chang-You LIU ◽  
Bao-Jie FAN ◽  
Zhi-Min CAO ◽  
Yan WANG ◽  
Zhi-Xiao ZHANG ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Vigna Radiata ◽  
Linear Models ◽  
Agronomic Traits ◽  
Mixed Linear Models
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