The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services p...The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.展开更多
This paper selects the daily data of national oil prices from January 2, 2014 to February 28, 2019, establishes an ARMA (2, 0) model, and tests its residuals for ARCH effects. Finally, the TARCH (1, 1) model is determ...This paper selects the daily data of national oil prices from January 2, 2014 to February 28, 2019, establishes an ARMA (2, 0) model, and tests its residuals for ARCH effects. Finally, the TARCH (1, 1) model is determined to quantitatively analyze the volatility of the crude oil market.展开更多
Non-stationary time series could be divided into piecewise stationary stochastic signal. However, the number and locations of breakpoints, as well as the approximation function of the respective segment signal are unk...Non-stationary time series could be divided into piecewise stationary stochastic signal. However, the number and locations of breakpoints, as well as the approximation function of the respective segment signal are unknown. To solve this problem, a novel on-line structural breaks estimation algorithm based on piecewise autoregressive processes is proposed. In order to find the "best" combination of the number, lengths, and orders of the piecewise autoregressive (AR) processes, the Akaikes Information Criterion (AIC) and Yule-Walker equations are applied to estimate an AR model fit to the data. Numerical results demonstrate that the proposed estimation algorithm is suitable for different data series. Furthermore, the algorithm is used in a clinical study of electroencephalogram (EEG) with satisfactory results, and the ability to deal with real-time data is the most outstanding characteristic of on-line structural breaks estimation algorithm proposed.展开更多
A Chinese speaker recognition system, which only use speech material of nasal initials and is text-independent, is presented in this paper. According to the properties of speaker’s fixed nasal cavity and stable phary...A Chinese speaker recognition system, which only use speech material of nasal initials and is text-independent, is presented in this paper. According to the properties of speaker’s fixed nasal cavity and stable pharynx cavity when Chinese nasal initials is spoken and a few Chinese nasal initials (the total number of them is only 101 which consists of 53 Tn- and 48 n-), the spectrum parameters of zero and pole point coefficients of all Chinese nasal initials can be gotten by using ARMA model. The performance of this system for 20 speakers is as follows’ The correct recognition rate (CRR) is 87.92% for each speaker to test all initials, when randomly choosing 2, 3, 4 and 5 initials in each speaker’s and then averaging their spectrum to test individual template, the average’ CRRs are 91.67%, 95.00%, 96.67% and 99.97% respectively.展开更多
Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting s...Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load.Then,set order for the time series model by Akaike information criterion(AIC)rule and acquire model coefficients to establish ARMA(2,1)model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the electric power load data.Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA(2,1)model.Besides,variance is reduced by two orders,and every coefficient of stochastic noise is reduced by one order.The filter method based on time series model does reduce stochastic noise of electric power load,and increase measurement accuracy.展开更多
To study the groundwater dynamic in the typical region of Sanjiang Plain, long-term groundwater level observation data in the Honghe State Farm were collected and analyzed in this paper. The seasonal and long-term gro...To study the groundwater dynamic in the typical region of Sanjiang Plain, long-term groundwater level observation data in the Honghe State Farm were collected and analyzed in this paper. The seasonal and long-term groundwater dynamic was explored. From 1996 to 2008, groundwater level kept declining due to intensive exploitation of groundwater resources for rice irrigation. A decline of nearly 5 m was found for almost all the monitoring wells. A time-series method was established to model the groundwater dynamic. Modeled results by time-series model showed that the groundwater level in this region would keep declining according to the current exploitation intensity. A total dropdown of 1.07 m would occur from 2009 to 2012. Time-series model can be used to model and forecast the groundwater dynamic with high accuracy. Measures including control on groundwater exploitation amount and application of water saving irrigation technique should be taken to prevent the continuing declining of groundwater in the Sanjiang Plain.展开更多
文摘The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.
文摘This paper selects the daily data of national oil prices from January 2, 2014 to February 28, 2019, establishes an ARMA (2, 0) model, and tests its residuals for ARCH effects. Finally, the TARCH (1, 1) model is determined to quantitatively analyze the volatility of the crude oil market.
基金supported by Fund of National Science & Technology monumental projects under Grants No. 2012ZX03005012, 2011ZX03005-004-03, 2009ZX03003-007
文摘Non-stationary time series could be divided into piecewise stationary stochastic signal. However, the number and locations of breakpoints, as well as the approximation function of the respective segment signal are unknown. To solve this problem, a novel on-line structural breaks estimation algorithm based on piecewise autoregressive processes is proposed. In order to find the "best" combination of the number, lengths, and orders of the piecewise autoregressive (AR) processes, the Akaikes Information Criterion (AIC) and Yule-Walker equations are applied to estimate an AR model fit to the data. Numerical results demonstrate that the proposed estimation algorithm is suitable for different data series. Furthermore, the algorithm is used in a clinical study of electroencephalogram (EEG) with satisfactory results, and the ability to deal with real-time data is the most outstanding characteristic of on-line structural breaks estimation algorithm proposed.
文摘A Chinese speaker recognition system, which only use speech material of nasal initials and is text-independent, is presented in this paper. According to the properties of speaker’s fixed nasal cavity and stable pharynx cavity when Chinese nasal initials is spoken and a few Chinese nasal initials (the total number of them is only 101 which consists of 53 Tn- and 48 n-), the spectrum parameters of zero and pole point coefficients of all Chinese nasal initials can be gotten by using ARMA model. The performance of this system for 20 speakers is as follows’ The correct recognition rate (CRR) is 87.92% for each speaker to test all initials, when randomly choosing 2, 3, 4 and 5 initials in each speaker’s and then averaging their spectrum to test individual template, the average’ CRRs are 91.67%, 95.00%, 96.67% and 99.97% respectively.
基金Science and Technology Project of SGCC(SGTJDK00DWJS1600014).
文摘Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load.Then,set order for the time series model by Akaike information criterion(AIC)rule and acquire model coefficients to establish ARMA(2,1)model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the electric power load data.Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA(2,1)model.Besides,variance is reduced by two orders,and every coefficient of stochastic noise is reduced by one order.The filter method based on time series model does reduce stochastic noise of electric power load,and increase measurement accuracy.
基金Under the auspices of the Projects of the National Basis Research Program of China (2009CB421103)the Key Direction Program of the Chinese Academy of Science (KZCX2-YW-309-04, KZCX2-YW-Q06-03)National Natural Science Foundation of China(41001050)
文摘To study the groundwater dynamic in the typical region of Sanjiang Plain, long-term groundwater level observation data in the Honghe State Farm were collected and analyzed in this paper. The seasonal and long-term groundwater dynamic was explored. From 1996 to 2008, groundwater level kept declining due to intensive exploitation of groundwater resources for rice irrigation. A decline of nearly 5 m was found for almost all the monitoring wells. A time-series method was established to model the groundwater dynamic. Modeled results by time-series model showed that the groundwater level in this region would keep declining according to the current exploitation intensity. A total dropdown of 1.07 m would occur from 2009 to 2012. Time-series model can be used to model and forecast the groundwater dynamic with high accuracy. Measures including control on groundwater exploitation amount and application of water saving irrigation technique should be taken to prevent the continuing declining of groundwater in the Sanjiang Plain.
