Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can ...
ESN-Autoencoder可视化时间序列 这篇论文是发表在neurocomputing上的Model-Coupled Autoencoder for Time Series Visualisation,提出了通过结合Echo State Network (ESN)和Autoencoder来可视化时间序列的方法。可视化是将不同种类的时序数据映射(压缩)到低维(如二维)空间上,同一类的距离近,而不同类的距离远。 文章提出的...
loss_record = [] cnt = 0 tqdm_bar = tqdm(train_loader) for x in tqdm_bar: optimizer.zero_grad() # Set gradient to zero. x = x.to(device) # Move your data to device. x = x.view(x.shape[0],-1) pred = model(x) loss = criterion(pred, x) loss.backward() # Compute gr...
从training timeseries数据文件中获取数据值,并对值数据进行规范化。我们有一个14天内每天5分钟的值。 24 * 60 / 5 = 288 timesteps per day 288 * 14 = 4032 data points in total In [ ] # Normalize and save the mean and std we get, # for normalizing test data. training_mean = df_small...
Predictive variational autoencoder for learning robust representations of time-series data 来自 arXiv.org 喜欢 0 阅读量: 1 作者:JH Wang,D Tsin,T Engel 摘要: Variational autoencoders (VAEs) have been used extensively to discover low-dimensional latent factors governing neural activity and animal ...
The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long-short-term memory (LSTM) network to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior generation....
process_data.ipynb: Reformats and standardizes the data for use in the VAE. vae.ipynb: Trains the VAE and generates time series. plots.ipynb: Plot comparison of generated time series to real data. Results Sampling from the latent space of the model, I generated unique, compelling time series...
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which features are most salient in defining the observed dynamics. While recent work from our group and others has demonstrated the utility of ...
We discuss the important factors affecting performance of the system in applying the auto encoder to the time-series analysis of DCE-MRI medical image data. 展开 关键词: DCE-MRI deep-learning autoencoder time-series analysis medical image analysis cancer liver ...
[] for i in range(n): # if the difference at particular point > the average difference if so[i] > mso: # average value of 'c' neighbors on both sides nav = np.average(data[max(i - c, 0):min(i + c, n - 1)]) # add the local difference (difference of the point and ...