平均而言,0.5%的数据集是异常的。 5. NYCT:NYC Taxi Dataset 这是一个单变量时间序列数据集,包含2014-07-01至2015-01-31年间纽约市(NYC)出租车需求,每半小时记录一次乘客数量,包含10320个时间戳。它来自Numenta异常基准(NAB),该基准是评估异常检测算法的基准,尤其是流式数据。它包含五个集体异常,发生在纽约马...
In this paper, we present an extensive collection of outlier/anomaly detection tasks to identify unusual series from a given time series dataset. The presented work is based on the popular UCR time series classification archive. In addition to the detection tasks, we provide curated benchmarks, ...
Furthermore, the performance of the proposed methods is evaluated through extensive simulation studies and applied to the Beijing multi-site air quality dataset to impute missing values and detect mean changes in the data.Multidisciplinary Digital Publishing InstituteMathematicsBoping Tian...
To address your second question in the comment section, I used the airquality dataset for demonstration: library(forecast) # Convert as time series airTS = ts(airquality) # Plot multivariate ts plot(airTS[,1:4]) # Run auto.arima on a single ts arima_fit = auto.arima(airTS[,3]) # For...
# dataset available here: https://github.com/vcerqueira/blog/tree/main/data buoy = pd.read_csv('data/smart_buoy.csv', skiprows=[1], parse_dates=['time']) # setting time as index buoy.set_index('time', inplace=True) # resampling to hourly data ...
control the accuracy loss. Note that we evaluate the accuracy of an algorithm at a UTS level and perform hypothesis testing at a dataset level. Because for a specific UTSD, the number of accuracy values (and) is not enough to perform hypothesis testing. For a dataset consisting ofmindividual...
Extensive experiments on a public anomaly detection dataset, and deployment in a real-world medium and low voltage distribution system show the superiority of our proposed framework over state-of-the-arts.Keywords edge computinganomaly detectionunivariate time seriesself-attention ...
This concatenation repeats for all of the dataset. That is, if there are 𝑁N points in the dataset, there will be 𝑁−𝐷+1N−D+1 of these input-output pairs (Figure 1a). Note that it is possible to use only a subset of depths from 1 to 𝐷D, i.e., 𝐷D might be ...
Fitting a non-linear univariate regression to time-series data I've recently started machine learning using python. Below is a dataset I picked up as an example along with the codes I've worked on till now. Chosen [2000...2015] as the test data and train data [2016, 2017]. ...
A possible solution to this problem is to select one best forecast model for all the series in the dataset. Alternatively one may develop a rule that will select the best model for each series. Fildes (1989) calls the former an aggregate selection rule and the latter an individual selection...