scholarly journals Estimating Predictive Rate–Distortion Curves via Neural Variational Inference

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 640 ◽  
Author(s):  
Michael Hahn ◽  
Richard Futrell
Keyword(s):  
Natural Language ◽  
Time Series Data ◽  
Estimation Method ◽  
Rate Distortion ◽  
Estimation Methods ◽  
Series Data ◽  
Natural Languages ◽  
Complex Processes ◽  
Causal States ◽  
Predictive Rate

The Predictive Rate–Distortion curve quantifies the trade-off between compressing information about the past of a stochastic process and predicting its future accurately. Existing estimation methods for this curve work by clustering finite sequences of observations or by utilizing analytically known causal states. Neither type of approach scales to processes such as natural languages, which have large alphabets and long dependencies, and where the causal states are not known analytically. We describe Neural Predictive Rate–Distortion (NPRD), an estimation method that scales to such processes, leveraging the universal approximation capabilities of neural networks. Taking only time series data as input, the method computes a variational bound on the Predictive Rate–Distortion curve. We validate the method on processes where Predictive Rate–Distortion is analytically known. As an application, we provide bounds on the Predictive Rate–Distortion of natural language, improving on bounds provided by clustering sequences. Based on the results, we argue that the Predictive Rate–Distortion curve is more useful than the usual notion of statistical complexity for characterizing highly complex processes such as natural language.

EFFECT OF OUTLIERS ON THE FRACTAL DIMENSION ESTIMATION

Fractals ◽  
2011 ◽  
Vol 19 (02) ◽  
pp. 233-241
Author(s):  
SHAPOUR MOHAMMADI
Keyword(s):  
Fractal Dimension ◽  
Stock Prices ◽  
Time Series Data ◽  
Estimation Method ◽  
Estimation Methods ◽  
Series Data ◽  
Data Series ◽  
Box Dimension ◽  
Dimension Estimation ◽  
Stochastic Data

The effect of outliers on estimation of the fractal dimension of experimental chaotic and stock market stochastic data series is investigated. The results indicate that influential observations of a magnitude of mean ±5 standard deviations can lead to a distortion of fractal dimension estimations by as much as 40% for short (e.g. 500 observations) time series data. Moreover, the box dimension estimation method is more sensitive to outliers than information and correlation dimension estimation methods and the effect of outliers decreases as the number of observations increases. Application of outlier adjustment to the stock prices of 60 companies of the Dow Jones Industrial Index reveals that the effect of outliers is critical in estimating the fractal dimension. The fractal dimension has applications in risk analysis for financial markets that can be affected by outliers.

Adaptive Hyperbolic Asymmetric Power ARCH (A-HY-APARCH) model: Stability and Estimation

2020 ◽  
Vol 15 (4) ◽  
pp. 2511-2528
Author(s):  
Charline Uwilingiyimana ◽  
Abdou Kâ Diongue ◽  
Carlos Ogouyandjou
Keyword(s):  
Structural Changes ◽  
Time Series Data ◽  
Financial Time Series ◽  
Sufficient Conditions ◽  
Estimation Method ◽  
Estimation Methods ◽  
Series Data ◽  
Type Model ◽  
Model Stability ◽  
Asymmetric Power

In this paper, a new asymmetric GARCH type model that generalizes the Hyperbolic Asymmetric Power ARCH (HY-APARCH) process is proposed. The proposed model takes into consideration some characteristics of financial time series data like volatility clustering, long memory and structural changes. The necessary and sufficient conditions for the asymptotic stability of the model are derived and parameter estimation methods are proposed. The Monte Carlo Simulations are done to prove the performance of the estimation method

Democratic peace: Does ethnic inclusiveness reduce interstate conflict?

2019 ◽  
pp. 019251211988473
Author(s):  
Seung-Whan Choi ◽  
Henry Noll
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Estimation Method ◽  
Interstate Conflict ◽  
Model Specification ◽  
Series Data ◽  
Democratic Peace ◽  
Interstate Disputes ◽  
Time Series Data Analysis ◽  
Cross National

In this study, we argue that ethnic inclusiveness is an important democratic norm that fosters interstate peace. When two states are socialized into the notion of ethnic tolerance, they acquire the ability to reach cooperative arrangements in time of crisis. Based on cross-national time-series data analysis covering the period 1950–2001, we illustrate how two states that are inclusive of their politically relevant ethnic groups are less likely to experience interstate disputes than states that remain exclusive. This finding was robust, regardless of sample size, intensity of the dispute, model specification, or estimation method. Therefore, we believe in the existence of ethnic peace: ethnic inclusiveness represents an unambiguous force for democratic peace.

Changepoint Analysis of Binary and Ordinal Probit Models: An Application to Bank Rate Policy Under the Interwar Gold Standard

2011 ◽  
Vol 19 (2) ◽  
pp. 188-204 ◽  
Author(s):  
Jong Hee Park
Keyword(s):  
Time Series ◽  
Gold Standard ◽  
Time Series Data ◽  
Likelihood Function ◽  
Marginal Likelihood ◽  
Estimation Method ◽  
Simulated Data ◽  
Likelihood Method ◽  
Series Data ◽  
Ordinal Probit

In this paper, I introduce changepoint models for binary and ordered time series data based on Chib's hidden Markov model. The extension of the changepoint model to a binary probit model is straightforward in a Bayesian setting. However, detecting parameter breaks from ordered regression models is difficult because ordered time series data often have clustering along the break points. To address this issue, I propose an estimation method that uses the linear regression likelihood function for the sampling of hidden states of the ordinal probit changepoint model. The marginal likelihood method is used to detect the number of hidden regimes. I evaluate the performance of the introduced methods using simulated data and apply the ordinal probit changepoint model to the study of Eichengreen, Watson, and Grossman on violations of the “rules of the game” of the gold standard by the Bank of England during the interwar period.

Impact of Fiscal Policy on Agricultural Output in Nigeria

2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
Author(s):  
Adedoyin Isola LAWAL ◽  
Ernest Onyebuchi FIDELIS ◽  
Abiola Ayoopo BABAJIDE ◽  
Barnabas O. OBASAJU ◽  
Oluwatoyese OYETADE ◽  
...  
Keyword(s):  
Fiscal Policy ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Negative Impact ◽  
Capital Expenditure ◽  
Research Work ◽  
Estimation Method ◽  
Series Data ◽  
Agricultural Output ◽  
The Impact

This study examines the impact of fiscal policy on agricultural output in Nigeria using the most recent official data. The metrics for fiscal policy is government capital expenditure and custom duties on fertilizer. The study used annual time series data obtained from CBN annual statistical bulletin, NCS, and FIRS which was found to be stationary at the order of I(1) and I(0). The order of unit root test led to the use of ARDL estimation method employed in the empirical analysis of this research work. The study found evidence of both short and long run relationship between the variables (VAO, GEX, IDMF, and ACGSF) using both Johansen co-integration and ARDL Bounds test. Although government expenditure (GEX) to agricultural sector was found to be statistically insignificant which recommend that government should increase agriculture capital expenditure to ensure that its contribution is significant. Consequently, custom duties on fertilizer (IDMF) was found to be negatively signed and significant indicating a negative impact on agricultural output. This demands that the policy makers should be prudent in the use of fiscal policy instrument in achieving its desired objective.

scholarly journals Cuffless Blood Pressure Estimation Based on Data-Oriented Continuous Health Monitoring System

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kengo Atomi ◽  
Haruki Kawanaka ◽  
Md. Shoaib Bhuiyan ◽  
Koji Oguri
Keyword(s):  
Blood Pressure ◽  
Time Series Data ◽  
Estimation Method ◽  
Series Data ◽  
Healthy Life ◽  
Blood Pressure Data ◽  
Pressure Estimation ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure ◽  
Lifestyle Related Diseases

Measuring blood pressure continuously helps monitor health and also prevent lifestyle related diseases to extend the expectancy of healthy life. Blood pressure, which is nowadays used for monitoring patient, is one of the most useful indexes for prevention of lifestyle related diseases such as hypertension. However, continuously monitoring the blood pressure is unrealistic because of discomfort caused by the tightening of a cuff belt. We have earlier researched the data-oriented blood pressure estimation without using a cuff. Remarkably, our blood pressure estimation method only uses a photoplethysmograph sensor. Therefore, the application is flexible for sensor locations and measuring situations. In this paper, we describe the implementation of our estimation method, the launch of a cloud system which can collect and manage blood pressure data measured by a wristwatch-type photoplethysmograph sensor, and the construction of our applications to visualize life-log data including the time-series data of blood pressure.

scholarly journals Environmental Pollution Effects of Regional Industrial Transfer Illustrated with Jiangsu, China

2021 ◽  
Vol 13 (21) ◽  
pp. 12128
Author(s):  
Guangxiong Mao ◽  
Wei Jin ◽  
Ying Zhu ◽  
Yanjun Mao ◽  
Wei-Ling Hsu ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Manufacturing Industry ◽  
Time Series Data ◽  
Estimation Method ◽  
Raw Material ◽  
Series Data ◽  
Pollution Load ◽  
Pollution Effects ◽  
Industrial Transfer ◽  
Southern Jiangsu

Industrial transfer is reshaping the geographic layout of industries and facilitating the transfer and spread of environmental pollution. This study employs the pollution transfer estimation method to discuss the environmental effect of industrial transfer. By compiling statistics on industries of a certain scale according to time-series data, the researchers compute the pollution load generated by industrial transfer and the difference in pollution emissions for each region and industry. Through the constructed evaluation model, the empirical scope is Jiangsu, which is the most developed industry in China. The results reveal that there is an apparent spatial hierarchy among the transferred industries in Jiangsu. Most industries transfer from the southern Jiangsu region toward the central Jiangsu and northern Jiangsu regions. Environmental pollution is redistributed among prefecture-level cities because of intercity industrial transfer; the spatial characteristics of pollution exhibit a notable hierarchical pattern. Furthermore, the transferred pollution load differs considerably between industries. The textile industry and chemical raw material and chemical product industry are mainly transferred toward the Central Jiangsu and Northern Jiangsu regions, whereas the papermaking and paper product manufacturing industry is primarily redistributed to the Southern Jiangsu region. The empirical results can serve as a reference for analyzing the environmental pollution effects of regional industrial transfer.

scholarly journals THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA

2017 ◽  
Vol 4 (2) ◽  
pp. 157 ◽  
Author(s):  
Andang Suryana Soma ◽  
Tetsuya Kubota
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Landslide Susceptibility ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Causative Factors ◽  
Predictive Rate

The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR), and Logistic regression (LR) method in a geographic information system. In the study area, Upper Ujung-loe Watersheds area of Indonesia, landslides were detected using field survey and air photography from time series data image of Google Earth Pro from 2012 to 2016 and LUC from 2004 to 2011. Landslide susceptibility map (LSM) was constructed using FR and LR with nine causative factors. The result indicated that LUC affect the production of LSM. Validation of landslide susceptibility was carried out in this study at both with and without LUC causative factors. First, performances of each landslide model were tested using AUC curve for success and predictive rate. The highest value of predictive rate at with LUC in both FR and LR method were 83.4 % and 85.2 %, respectively. In the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.LR method with LUC had the highest accuracy of 80.24 %. Taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence.

scholarly journals Tracking Poisson Parameter for Non-Stationary Discontinuous Time Series with Taylor’s Abnormal Fluctuation Scaling

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Gen Sakoda ◽  
Hideki Takayasu ◽  
Misako Takayasu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
State Space Model ◽  
Estimation Method ◽  
Stationary Time Series ◽  
Series Data ◽  
Large Fluctuations ◽  
Taylor’S Law ◽  
Fluctuation Scaling ◽  
Poisson Parameter

We propose a parameter estimation method for non-stationary Poisson time series with the abnormal fluctuation scaling, known as Taylor’s law. By introducing the effect of Taylor’s fluctuation scaling into the State Space Model with the Particle Filter, the underlying Poisson parameter’s time evolution is estimated correctly from given non-stationary time series data with abnormally large fluctuations. We also developed a discontinuity detection method which enables tracking the Poisson parameter even for time series including sudden discontinuous jumps. As an example of application of this new general method, we analyzed Point-of-Sales data in convenience stores to estimate change of probability of purchase of commodities under fluctuating number of potential customers. The effectiveness of our method for Poisson time series with non-stationarity, large discontinuities and Taylor’s fluctuation scaling is verified by artificial and actual time series.

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