In this paper we propose some dissimilarity measure functions for trends of nonstationary time series.If time series are stationary,the cross correlation function can be applied as a similarity measure.However,the val...In this paper we propose some dissimilarity measure functions for trends of nonstationary time series.If time series are stationary,the cross correlation function can be applied as a similarity measure.However,the validity of the cross correlation function is lost for nonstationary time series.Moreover,the cross correlation function cannot be calculated if one of trends is constant.The proposed functions can be applied even if trends are constant and their values are determined through the minimization.The clustering is considered as an application of the dissimilarity measure.Furthermore the problem of the common trend within multiple time series is considered and the estimation algorithm is proposed.Usability of the proposed method is demonstrated by applying to series of COVID-19 cases in Japan.展开更多
Even though over many years the IUCN has considered the African buffalo and waterbuck and abundant species in Africa with no conservation concern, the situation is rapidly changing. Using aerial counts in wet and dry ...Even though over many years the IUCN has considered the African buffalo and waterbuck and abundant species in Africa with no conservation concern, the situation is rapidly changing. Using aerial counts in wet and dry season in 2010 and 2013, this study assessed the trend, population status and distribution of the African buffalo and common waterbuck in the Northern Tanzania and Southern Kenya borderland. Both species were rare in the borderland, with the Amboseli region had the highest number of buffalo (241.5 ± 29.9), followed by Magadi/Namanga (58.0 ± 22.0), West Kilimanjaro (38.8 ± 34.9), and lastly Lake Natron (14.5 ± 9.0) areas. In terms of density, Amboseli also led with 0.03 ± 0.00 (buffalo per km2), but rest had similar densities of 0.01 ± 0.00 buffalo per km2. In terms of percent changes in buffalo, Amboseli area had a positive increase (+10.59 ± 27.71), but with a negative growth of -17.12 in the dry season. All other changes in all locations had negative (decline) buffalo numbers over time. For waterbuck numbers, Amboseli area also led with 12.3 ± 3.9 waterbuck), followed by Magadi/Namanga (10.3 ± 3.7.0), Lake Natron (3.8 ± 3.4), and lastly West Kilimanjaro (0.5 ± 0.5) areas. In terms of waterbuck density, they were low and less than 0.00 ± 0.00 per km2. For percent changes in waterbuck numbers, Magadi/Namanga had higher positive change (+458.33 ± 291.67), but all other locations had negative (decline) changes with the worst being West Kilimanjaro and Lake Natron areas. Further, buffalo number was dependent (p = 0.008) on the season, with numbers being higher in the wet season than dry season. For waterbuck, numbers were independent (p = 0.72) of the season, with numbers being similar across seasons. The findings of this study showed that both species were negatively affected by drought. We recommend a constant joint monitoring program between Kenya and Tanzania, and jointly combat poaching, habitat fragmentation and encroachment to build viable populations in the borderland.展开更多
运用共同趋势与共同周期（common trend and common cycles）理论，研究中国与东盟国家之间的经济周期在波动中是否存在同步性．以1994—2005年问中国与东盟的GDP季度时间序列为基础，建立了多变量向量误差修正模型．实证分析发现：中...运用共同趋势与共同周期（common trend and common cycles）理论，研究中国与东盟国家之间的经济周期在波动中是否存在同步性．以1994—2005年问中国与东盟的GDP季度时间序列为基础，建立了多变量向量误差修正模型．实证分析发现：中国与东盟国家GDP季度时间序列之间在短期内有共同周期，在长期里存在共同发展趋势；中国与东盟国家经济周期在考察样本期间具有同步性，满足金融合作最重要的前提条件；中国与东盟国家应当进一步强化彼此间的经贸交流和经济金融合作，真正发挥区域经济之间的相互带动作用，从而实现区域经济的共同繁荣．展开更多
文摘In this paper we propose some dissimilarity measure functions for trends of nonstationary time series.If time series are stationary,the cross correlation function can be applied as a similarity measure.However,the validity of the cross correlation function is lost for nonstationary time series.Moreover,the cross correlation function cannot be calculated if one of trends is constant.The proposed functions can be applied even if trends are constant and their values are determined through the minimization.The clustering is considered as an application of the dissimilarity measure.Furthermore the problem of the common trend within multiple time series is considered and the estimation algorithm is proposed.Usability of the proposed method is demonstrated by applying to series of COVID-19 cases in Japan.
文摘Even though over many years the IUCN has considered the African buffalo and waterbuck and abundant species in Africa with no conservation concern, the situation is rapidly changing. Using aerial counts in wet and dry season in 2010 and 2013, this study assessed the trend, population status and distribution of the African buffalo and common waterbuck in the Northern Tanzania and Southern Kenya borderland. Both species were rare in the borderland, with the Amboseli region had the highest number of buffalo (241.5 ± 29.9), followed by Magadi/Namanga (58.0 ± 22.0), West Kilimanjaro (38.8 ± 34.9), and lastly Lake Natron (14.5 ± 9.0) areas. In terms of density, Amboseli also led with 0.03 ± 0.00 (buffalo per km2), but rest had similar densities of 0.01 ± 0.00 buffalo per km2. In terms of percent changes in buffalo, Amboseli area had a positive increase (+10.59 ± 27.71), but with a negative growth of -17.12 in the dry season. All other changes in all locations had negative (decline) buffalo numbers over time. For waterbuck numbers, Amboseli area also led with 12.3 ± 3.9 waterbuck), followed by Magadi/Namanga (10.3 ± 3.7.0), Lake Natron (3.8 ± 3.4), and lastly West Kilimanjaro (0.5 ± 0.5) areas. In terms of waterbuck density, they were low and less than 0.00 ± 0.00 per km2. For percent changes in waterbuck numbers, Magadi/Namanga had higher positive change (+458.33 ± 291.67), but all other locations had negative (decline) changes with the worst being West Kilimanjaro and Lake Natron areas. Further, buffalo number was dependent (p = 0.008) on the season, with numbers being higher in the wet season than dry season. For waterbuck, numbers were independent (p = 0.72) of the season, with numbers being similar across seasons. The findings of this study showed that both species were negatively affected by drought. We recommend a constant joint monitoring program between Kenya and Tanzania, and jointly combat poaching, habitat fragmentation and encroachment to build viable populations in the borderland.
文摘运用共同趋势与共同周期（common trend and common cycles）理论，研究中国与东盟国家之间的经济周期在波动中是否存在同步性．以1994—2005年问中国与东盟的GDP季度时间序列为基础，建立了多变量向量误差修正模型．实证分析发现：中国与东盟国家GDP季度时间序列之间在短期内有共同周期，在长期里存在共同发展趋势；中国与东盟国家经济周期在考察样本期间具有同步性，满足金融合作最重要的前提条件；中国与东盟国家应当进一步强化彼此间的经贸交流和经济金融合作，真正发挥区域经济之间的相互带动作用，从而实现区域经济的共同繁荣．
