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Significant increases in computational resources have enabled the development of more complex and spatially better resolved weather and climate models. As a result the amount of output generated by data assimilation systems and by weather and climate simulations is rapidly increasing e.g. due to higher spatial resolution, more realisations and higher frequency data. However, while compute performance...
Time series modelling has long been used to make forecast in different industries with a variety of statistical models currently available. Methods for analyzing changing patterns of stock prices have always been based on fixed time series. Considering that these methods have ignored some crucial factors in stock prices, we use ARIMA model to predict stock prices given the stock-trading volume and...
Inflation forecasts becomes a key input of monetary policy decision. CPI is a measure of inflation, however, an important economic indicator. Based on the monthly CPI data from January 2000 to December 2009, the thesis firstly statistically indentifies the correlation function and the partial correlation function of consumer price index, tests the stationarity of ADF, then uses ARIMA model to test...
This paper presents time series analysis for short-term Singapore electricity demand forecasting. Two time series models are proposed, namely, the multiplicative decomposition model and the seasonal ARIMA Model. Forecasting errors of both models are computed and compared. Results show that both time series models can accurately predict the short-term Singapore demand and that the Multiplicative decomposition...
The tolerance and non-stability in financial indexes make changes to other sub-systems like human resources, economics, factory productions and etc. Having underling knowledge and a model to simulate such systems obtains a fine vision to estimate further and calculate hard-decision making tasks before execution like: dept from banks, cash injecting and insurance services. Using Neuro-fuzzy networks...
The analysis and modeling of high-frequency financial data are new research fields in financial econometrics. The realized covariance matrix, gotten by expanding realized volatility based on univariate high-frequency data to multivariate high-frequency data, can describe volatility and correlation of multivariate time series. The paper gains the realized covariance matrix of the high-frequency data...
This article firstly presents an analysis and survey regarding the traditional evaluation and forecasting model on fuzzy time series. lt is pointed out that the maximum Subordination degree method and Subordination degree-Weighted average method is not suitable to attribute space usually, and a new evaluation model is proposed. The empirical study show that the new evaluation model is better able...
This paper proposes an effective hybridization of grey relational analysis (GRA) and Backpropagation Particle Swarm Optimization (BP_PSO) for time series forecasting. The hybridization employs the complementary strength of these two appealing techniques. Additionally the combination of GRA and BP as cooperative feature selection (CFS) has successfully assessed the importance of each input variable...
In this paper an ARIMA model is used for time-series forecast involving wind speed measurements. Results are compared with the performance of a back propagation type NNT. Results show that ARIMA model is better than NNT for short time-intervals to forecast (10 minutes, 1 hour, 2 hours and 4 hours). Data was acquired from a unit located in Southern Andalusia (Pentildeaflor, Sevilla), with a soft orography...
In highly volatile market conditions it's always difficult to predict returns using heteroscedastic Garch models. This paper tries to investigate the impact of sample data inputs over forecast using nested conditional mean ARMAX(2, 2, 0) and conditional variance Garch(1, 1), Gjr-garch(1, 1) and Egarch(1, 1) models. Research also tries to indentify relationship between outcome of formal hypothesis...
Forecasting accuracy is the most important factor selecting any forecasting methods. Research for improving the accuracy of forecasting models has never been stopped. The idea in this paper is simple and old while the practice is straightforward using the software technology in use. We intend to filter out the residuals from a multivariate time series causality model by a univariate (residual term)...
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