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117 | [2960] | [0, 1] | 111 | 973 | a binary question | local | basic analysis | 根据CloudOps时间序列数据分析，索引180-250区间内ts1的显著下降（从约99%降至87%并维持稳定状态）最可能代表的是系统故障导致的服务中断还是计划内维护活动？ | A. 系统故障导致的服务中断
B. 计划内维护活动 | B. 计划内维护活动 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.9576305941455454, 'trend': 0.7299948293441747}, 'seasonality analysis': {'seasonal_strength': 0.9591551004678547, 'seas_acf1': 0.4707293352151027, 'peak': 78.0, 'trough': 215.0}, 'noise analysis': {'entropy': 0.397240782834061, 'residual_std': 0.8991823292638119, 'rmse': 0.9368407356011396}, 'volatility analysis': {'stability': 0.04098637279935627, 'lumpiness': 0.07001575877645473, 'arch_lm': 0.6363946950731202}, 'basic analysis': {'crossing_points': 88.0, 'nonlinearity': 0.4753324806640588, 'mean': 9.96872537659328, 'median': 9.07, 'q25': 7.3, 'q75': 11.9, 'iqr': 4.6000000000000005, 'std': 3.5279177577685266, 'min': 4.43, 'max': 24.53, 'range': 20.1}}, 'ts1': {'trend analysis': {'hurst': 1.0475834035260252, 'trend': 0.7121716911930218}, 'seasonality analysis': {'seasonal_strength': 0.8539448495142563, 'seas_acf1': -0.031743855056654516, 'peak': 2.0, 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271 | [7605] | [0, 1] | 0 | 1,726 | a binary question | overall | seasonality analysis | 根据CloudOps时间序列数据的观察，ts1是否比ts0展示出更强的季节性自相关特征？ | A. 是的，ts1展示出更强的季节性自相关特征
B. 不是，ts0展示出更强的季节性自相关特征 | A. 是的，ts1展示出更强的季节性自相关特征 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.601697775264392, 'trend': 0.0979652686018021}, 'seasonality analysis': {'seasonal_strength': 0.491446420319841, 'seas_acf1': 0.0953766033718387, 'peak': 168.0, 'trough': 168.0}, 'noise analysis': {'entropy': 0.7840212895142115, 'residual_std': 0.2890374032936063, 'rmse': 0.292229843813677}, 'volatility analysis': {'stability': 0.021284998086356217, 'lumpiness': 1.1710943242369973, 'arch_lm': 0.010179835944376436}, 'basic analysis': {'crossing_points': 348.0, 'nonlinearity': 2.310681988168398, 'mean': 2.6247307469600463, 'median': 2.57, 'q25': 2.5, 'q75': 2.67, 'iqr': 0.16999999999999993, 'std': 0.3100955731760444, 'min': 2.2, 'max': 9.71, 'range': 7.510000000000001}}, 'ts1': {'trend analysis': {'hurst': 0.9956935044546508, 'trend': 0.9713961912605785}, 'seasonality analysis': {'seasonal_strength': 0.3810883101343465, 'seas_acf1': 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441 | [12398] | [0, 1] | 284 | 1,146 | a binary question | local | trend analysis | ：
根据CloudOps数据集中索引284到1146期间的时间序列分析，ts0是否比ts1展示出更强的周期性趋势特征？ | ：
A. 是的，ts0展示出更强的周期性趋势特征
B. 不是，ts1展示出更强的周期性趋势特征 | ：
A. 是的，ts0展示出更强的周期性趋势特征 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.8057182350205849, 'trend': 0.4133484534614984}, 'seasonality analysis': {'seasonal_strength': 0.9914451060371694, 'seas_acf1': 0.6627752221260083, 'peak': 259.0, 'trough': 56.0}, 'noise analysis': {'entropy': 0.1477523427331308, 'residual_std': 1.227248068060206, 'rmse': 1.242946361982858}, 'volatility analysis': {'stability': 0.0074620099505005285, 'lumpiness': 0.007296489275942029, 'arch_lm': 0.8976677540621272}, 'basic analysis': {'crossing_points': 42.0, 'nonlinearity': 0.5689095289906878, 'mean': 35.218864426419465, 'median': 39.17, 'q25': 24.015, 'q75': 43.665000000000006, 'iqr': 19.650000000000006, 'std': 11.149425723309859, 'min': 16.03, 'max': 57.5, 'range': 41.47}}, 'ts1': {'trend analysis': {'hurst': 0.7017494969666244, 'trend': 0.09406150504454225}, 'seasonality analysis': {'seasonal_strength': 0.5955481371941441, 'seas_acf1': 0.07108013325511868, 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501 | [14375] | [0, 1] | 0 | 1,726 | a multiple choice question | overall | volatility analysis | 根据CloudOps监控中的两个时间序列(ts0和ts1)的波动特性，下列哪一项关于它们稳定性(stability)、波动均匀性(lumpiness)和波动聚集性(conditional heteroskedasticity)的描述是正确的？ | A. ts0的波动均匀性高于ts1，表现为整个时间序列中波动强度一致，适合使用静态阈值进行监控
B. ts1的波动聚集性(conditional heteroskedasticity)高于ts0，需要使用GARCH族模型进行预测
C. ts0表现出明显的平台期和突然跳跃，波动聚集性较高，而ts1波动频繁但幅度小且模式相对一致
D. ts0和ts1都表现出较高的稳定性(stability)，它们在不同时间窗口的平均水平变化相近 | C | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.8172663139391965, 'trend': 0.34127888102098314}, 'seasonality analysis': {'seasonal_strength': 0.614714739831774, 'seas_acf1': 0.27583312171883545, 'peak': 135.0, 'trough': 149.0}, 'noise analysis': {'entropy': 0.5756730179102957, 'residual_std': 4.312190616087771, 'rmse': 4.365138272227891}, 'volatility analysis': {'stability': 0.12287371370556763, 'lumpiness': 0.11830150271967849, 'arch_lm': 0.703859610596334}, 'basic analysis': {'crossing_points': 176.0, 'nonlinearity': 0.14031989392972977, 'mean': 45.169530978575565, 'median': 44.77, 'q25': 41.115, 'q75': 48.37, 'iqr': 7.2549999999999955, 'std': 6.01851475156132, 'min': 26.27, 'max': 70.43, 'range': 44.16000000000001}}, 'ts1': {'trend analysis': {'hurst': 0.7495038229959966, 'trend': 0.050071291180241784}, 'seasonality analysis': {'seasonal_strength': 0.41596584962483885, 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535 | [15285] | [0, 1] | 0 | 1,726 | a binary question | overall | trend analysis | 在CloudOps监控数据中，ts0和ts1这两个时间序列是否具有相同类型的趋势特性？ | A. 是的，两个时间序列都表现出明显的趋势特性，只是表现形式不同
B. 不是，ts0表现为稀疏的脉冲型数据几乎没有趋势性，而ts1表现为连续数值且具有明显的长期趋势 | B. 不是，ts0表现为稀疏的脉冲型数据几乎没有趋势性，而ts1表现为连续数值且具有明显的长期趋势 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': None, 'trend': 0.007672726101471294}, 'seasonality analysis': {'seasonal_strength': 0.35978027238438903, 'seas_acf1': 0.11524320246781425, 'peak': 184.0, 'trough': 98.0}, 'noise analysis': {'entropy': 0.9429941196043247, 'residual_std': 0.004101728696469469, 'rmse': 0.004118960591049495}, 'volatility analysis': {'stability': 0.005493194508098241, 'lumpiness': 1.8213774472640691, 'arch_lm': 0.003929818860385348}, 'basic analysis': {'crossing_points': 34.0, 'nonlinearity': 0.007491465514069077, 'mean': 0.00037637521713954834, 'median': 0.0, 'q25': 0.0, 'q75': 0.0, 'iqr': 0.0, 'std': 0.00410172870957878, 'min': 0.0, 'max': 0.1, 'range': 0.1}}, 'ts1': {'trend analysis': {'hurst': 1.103772464820832, 'trend': 0.9127501380384805}, 'seasonality analysis': {'seasonal_strength': 0.4200703582165489, 'seas_acf1': 0.3852768065265076, 'peak': 286.0, 'trough': 242.0}, 'noise analysis': {'entropy': 0.1787862444511794, 'residual_std': 0.7171271006163583, 'rmse': 0.7172567605582312}, 'volatility analysis': {'stability': 1.1567763918619893, 'lumpiness': 0.019687273310735647, 'arch_lm': 0.9089371178294268}, 'basic analysis': {'crossing_points': 46.0, 'nonlinearity': 0.0468778961384206, 'mean': 95.8758425014476, 'median': 96.0, 'q25': 94.0, 'q75': 97.9, 'iqr': 3.9000000000000057, 'std': 2.1243517283801387, 'min': 92.0, 'max': 100.0, 'range': 8.0}}} | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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625 | [17794] | [0, 1] | 821 | 1,683 | a multiple choice question | local | volatility analysis | 在CloudOps监控数据分析中，关于索引821到1683时间段内的ts0和ts1两个时间序列的波动特性，以下哪项描述是正确的？ | A. ts0的波动更为稳定，而ts1展现出较大的波动幅度和频繁的极端值
B. ts0和ts1都表现出相似的stability和lumpiness特性，只是数值范围不同
C. ts0展现出较大的波动幅度和多个峰值，而ts1虽然整体较稳定但具有更强的volatility clustering特性
D. ts1的波动主要集中在95-97之间，偶尔会上升到98-100的水平 | C | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.6889491313577657, 'trend': 0.05384762549900046}, 'seasonality analysis': {'seasonal_strength': 0.7077749227449167, 'seas_acf1': 0.06925498399505024, 'peak': 242.0, 'trough': 16.0}, 'noise analysis': {'entropy': 0.8690553828209608, 'residual_std': 1.827478393416917, 'rmse': 1.8276911937265565}, 'volatility analysis': {'stability': 0.02606883461747223, 'lumpiness': 0.03917246215456076, 'arch_lm': 0.0949106329202064}, 'basic analysis': {'crossing_points': 410.0, 'nonlinearity': 0.14694861467369008, 'mean': 6.891181923522596, 'median': 6.07, 'q25': 5.13, 'q75': 7.715, 'iqr': 2.585, 'std': 2.5003375425520566, 'min': 4.27, 'max': 27.43, 'range': 23.16}}, 'ts1': {'trend analysis': {'hurst': 0.6376873732995536, 'trend': 0.03457362522073204}, 'seasonality analysis': {'seasonal_strength': 0.7147407330021291, 'seas_acf1': 0.057301023668253986, 'peak': 204.0, 'trough': 69.0}, 'noise analysis': {'entropy': 0.7750396875682494, 'residual_std': 0.38580727600319703, 'rmse': 0.38637761688941785}, 'volatility analysis': {'stability': 0.0015771304802673564, 'lumpiness': 0.007425126146991344, 'arch_lm': 0.35084668986985657}, 'basic analysis': {'crossing_points': 268.0, 'nonlinearity': 1.167471283620056, 'mean': 99.16176129779838, 'median': 99.03, 'q25': 99.0, 'q75': 99.63, 'iqr': 0.6299999999999955, 'std': 0.5832824792097948, 'min': 97.0, 'max': 100.0, 'range': 3.0}}} | [[9.6, 9.77, 9.37, 4.47, 7.53, 4.63, 4.73, 7.63, 5.1, 8.6, 8.53, 13.6, 12.93, 9.47, 5.03, 5.0, 5.4, 6.33, 5.17, 8.77, 5.93, 6.4, 17.43, 12.1, 5.77, 5.0, 4.93, 5.2, 6.07, 5.62, 4.5, 12.0, 5.87, 5.03, 5.6, 7.45, 4.8, 7.07, 5.48, 4.4, 6.17, 8.63, 6.07, 6.3, 11.0, 9.03, 11.6, 5.27, 4.6, 5.03, 4.37, 5.6, 4.53, 8.37, 4.57, 11.47, 4.33, 4.37, 5.27, 5.33, 5.63, 6.5, 6.17, 9.63, 10.1, 8.0, 15.33, 12.93, 5.67, 5.33, 6.03, 5.87, 4.87, 6.8, 9.33, 7.3, 5.03, 6.3, 5.0, 9.07, 5.37, 4.93, 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677 | [18932] | [0, 1] | 341 | 1,203 | a binary question | local | noise analysis | 在CloudOps监控系统中，观察索引830附近的异常现象，ts0出现峰值(44.2)的同时ts1出现显著下跌(降至约69%)。基于这两个时间序列的整体表现和异常模式，以下哪个陈述是正确的？ | A. ts0的时间序列比ts1更容易预测，因为ts0的熵值更低，表明其频域能量更集中。
B. ts1的时间序列比ts0更容易预测，尽管ts1的熵值更高，但其实际波动范围更小且更稳定。 | B. ts1的时间序列比ts0更容易预测，尽管ts1的熵值更高，但其实际波动范围更小且更稳定。 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.7299817955809021, 'trend': 0.08853463476347245}, 'seasonality analysis': {'seasonal_strength': 0.8547353456076837, 'seas_acf1': 0.5124328987645327, 'peak': 211.0, 'trough': 270.0}, 'noise analysis': {'entropy': 0.43318228623831234, 'residual_std': 0.6153414355480378, 'rmse': 0.6232119415809226}, 'volatility analysis': {'stability': 0.003381358639021657, 'lumpiness': 0.22500032201628795, 'arch_lm': 0.027222680225864937}, 'basic analysis': {'crossing_points': 62.0, 'nonlinearity': 2.495948097489592, 'mean': 12.160892236384703, 'median': 12.76, 'q25': 10.43, 'q75': 13.7, 'iqr': 3.2699999999999996, 'std': 2.64034870168443, 'min': 7.31, 'max': 44.2, 'range': 36.89}}, 'ts1': {'trend analysis': {'hurst': None, 'trend': 0.02407568281580852}, 'seasonality analysis': {'seasonal_strength': 0.3866846644685057, 'seas_acf1': -0.003339348333050272, 'peak': 95.0, 'trough': 211.0}, 'noise analysis': {'entropy': 0.9011683796836213, 'residual_std': 0.38224758932054015, 'rmse': 0.4031877121679512}, 'volatility analysis': {'stability': 0.009737853720510795, 'lumpiness': 3.557521225923238, 'arch_lm': 0.09912533970932502}, 'basic analysis': {'crossing_points': 386.0, 'nonlinearity': 0.5552054322673893, 'mean': 99.10271147161066, 'median': 99.23, 'q25': 99.0, 'q75': 99.53, 'iqr': 0.5300000000000011, 'std': 1.4258269358286266, 'min': 68.93, 'max': 100.0, 'range': 31.069999999999993}}} | [[8.1, 7.93, 8.17, 8.0, 7.76, 8.13, 8.07, 8.64, 8.57, 8.47, 8.11, 8.1, 7.96, 8.48, 8.6, 8.67, 8.86, 9.07, 9.03, 9.73, 9.68, 9.9, 9.73, 10.2, 11.47, 16.86, 10.43, 11.13, 10.89, 10.76, 10.73, 11.03, 11.1, 10.97, 11.03, 10.89, 11.03, 11.47, 11.6, 11.5, 11.57, 12.13, 11.79, 13.0, 12.55, 12.1, 12.63, 12.9, 13.6, 13.47, 13.23, 13.4, 13.31, 13.21, 13.03, 14.63, 14.33, 13.87, 14.37, 13.9, 14.17, 14.29, 13.7, 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729 | [20114] | [0, 1] | 0 | 1,726 | a binary question | overall | volatility analysis | 根据CloudOps监控系统中的两个时间序列ts0和ts1的图表分析，ts1的时间序列是否比ts0表现出更明显的状态切换特性（即离散的阶梯状模式而非连续波动）？ | A. 是的，ts1表现出更明显的状态切换特性
B. 不是，ts0表现出更明显的状态切换特性 | A. 是的，ts1表现出更明显的状态切换特性 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.8636838460974591, 'trend': 0.09853661555507975}, 'seasonality analysis': {'seasonal_strength': 0.5390609069359342, 'seas_acf1': 0.1773663480544447, 'peak': 42.0, 'trough': 284.0}, 'noise analysis': {'entropy': 0.8261167112789336, 'residual_std': 0.07516467707867742, 'rmse': 0.07528463583706413}, 'volatility analysis': {'stability': 0.026584232565166857, 'lumpiness': 0.6303001186218643, 'arch_lm': 0.006339666771530417}, 'basic analysis': {'crossing_points': 618.0, 'nonlinearity': 0.3972741204164046, 'mean': 0.3898147075854083, 'median': 0.37, 'q25': 0.33, 'q75': 0.43, 'iqr': 0.09999999999999998, 'std': 0.09746449981427051, 'min': 0.14, 'max': 1.73, 'range': 1.5899999999999999}}, 'ts1': {'trend analysis': {'hurst': None, 'trend': 0.9293828425493237}, 'seasonality analysis': {'seasonal_strength': 0.20755163206301486, 'seas_acf1': 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825 | [22589] | [0, 1] | 24 | 886 | a multiple choice question | local | basic analysis | 在索引24-886的CloudOps时间序列数据中，ts1变量呈现出什么样的主要模式特征？ | A. 高频振荡模式，表现为数值在均值附近随机波动
B. 阶梯状上升模式，具有长时间的稳定平台期和明确的状态转换点
C. 周期性循环模式，表现为规律性的峰值和谷值交替出现
D. 线性下降趋势，伴随小幅度的随机噪声波动 | B. 阶梯状上升模式，具有长时间的稳定平台期和明确的状态转换点 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': None, 'trend': 0.006924110879600653}, 'seasonality analysis': {'seasonal_strength': 0.3471281098760337, 'seas_acf1': -0.004725781279364401, 'peak': 175.0, 'trough': 211.0}, 'noise analysis': {'entropy': 0.9983323317049927, 'residual_std': 0.0871121345193274, 'rmse': 0.08717581628100601}, 'volatility analysis': {'stability': 0.007863794976923487, 'lumpiness': 4.421590373875052, 'arch_lm': 1.7335954489605676e-05}, 'basic analysis': {'crossing_points': 316.0, 'nonlinearity': 0.0005376854026242947, 'mean': 1.1723174971031285, 'median': 1.14, 'q25': 1.1, 'q75': 1.17, 'iqr': 0.06999999999999984, 'std': 0.9015207732081567, 'min': 1.0, 'max': 27.53, 'range': 26.53}}, 'ts1': {'trend analysis': {'hurst': None, 'trend': 0.9661196174130322}, 'seasonality analysis': {'seasonal_strength': 0.41106041083245226, 'seas_acf1': 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833 | [22872] | [0, 1] | 392 | 1,254 | a multiple choice question | local | seasonality analysis | 在CloudOps监控数据中，对比第392个到第1254个采样点期间的ts0和ts1时间序列，下列关于这两个指标季节性特征的描述哪个是正确的？ | A. ts0的季节性强度高于ts1，并且ts0具有更明显的阶梯状分布模式
B. ts1展示出更强的季节性模式，但ts0的季节性自相关系数略高
C. 两个时间序列的季节性强度相近，但ts0的波动幅度（peak-trough差值）更大
D. ts1的季节性波动更为平缓，适合使用简单的线性预测模型 | B. ts1展示出更强的季节性模式，但ts0的季节性自相关系数略高 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.9113971727741793, 'trend': 0.4352981581539369}, 'seasonality analysis': {'seasonal_strength': 0.881436636640258, 'seas_acf1': 0.40917157742794713, 'peak': 219.0, 'trough': 208.0}, 'noise analysis': {'entropy': 0.6479102728544349, 'residual_std': 0.19638708919282788, 'rmse': 0.20951954931889943}, 'volatility analysis': {'stability': 0.1551046685299138, 'lumpiness': 0.08112425072140104, 'arch_lm': 0.06558697358916699}, 'basic analysis': {'crossing_points': 142.0, 'nonlinearity': 1.9969024481819795, 'mean': 6.108041714947856, 'median': 6.1, 'q25': 5.83, 'q75': 6.4, 'iqr': 0.5700000000000003, 'std': 0.43501893313742185, 'min': 5.07, 'max': 9.03, 'range': 3.959999999999999}}, 'ts1': {'trend analysis': {'hurst': 0.9387030176759219, 'trend': 0.8680889746094403}, 'seasonality analysis': {'seasonal_strength': 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859 | [23697] | [0, 1] | 325 | 1,187 | a multiple choice question | local | trend analysis | 在CloudOps监控系统中，索引780附近两个指标ts0和ts1同时出现异常变化（ts0峰值，ts1断崖），从两个时间序列的整体行为模式来看，以下哪种监控策略组合最适合这两个指标？ | A. 对ts0和ts1都采用基于静态阈值的监控策略
B. 对ts0采用波动监控策略，对ts1采用趋势偏离监控策略
C. 对ts0采用趋势偏离监控策略，对ts1采用波动监控策略
D. 对ts0和ts1都采用基于移动平均的短期波动监控策略 | B. 对ts0采用波动监控策略，对ts1采用趋势偏离监控策略 | cloudops | 5T | false | lotsa_data/alibaba_cluster_trace_2018 | {'ts0': {'trend analysis': {'hurst': 0.7667180337317172, 'trend': 0.07602099491365943}, 'seasonality analysis': {'seasonal_strength': 0.9448426934290427, 'seas_acf1': 0.6187685315491438, 'peak': 84.0, 'trough': 285.0}, 'noise analysis': {'entropy': 0.2430964460743557, 'residual_std': 0.289233660404555, 'rmse': 0.2976626820240445}, 'volatility analysis': {'stability': 0.00045481143252084754, 'lumpiness': 0.06564356716428256, 'arch_lm': 0.3940579634349337}, 'basic analysis': {'crossing_points': 46.0, 'nonlinearity': 1.1914218332473836, 'mean': 7.4627346465816915, 'median': 7.43, 'q25': 5.9, 'q75': 9.0, 'iqr': 3.0999999999999996, 'std': 1.6695232652059118, 'min': 4.6, 'max': 17.57, 'range': 12.97}}, 'ts1': {'trend analysis': {'hurst': 1.0973565265108645, 'trend': 0.7401320132068092}, 'seasonality analysis': {'seasonal_strength': 0.467364507454454, 'seas_acf1': 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