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Uncovering Hidden Patterns: Time Series Analysis Techniques

Time series data, which comprises observations collected over sequential time intervals, is ubiquitous in various fields like finance, economics, climate ....

Recurrent Neural Networks Rnns , Time Series , Defining Time Series , Time Series Data , Moving Averages , Exponential Smoothing , Decomposition Components , Integrated Moving Average , Parameter Selection , Frequency Domain Analysis , Spectral Analysis , Supervised Learning Approaches , Recurrent Neural Networks , Statistical Methods , Machine Learning Based Approaches , Isolation Forest , Performance Metrics , Mean Absolute Error , Root Mean Squared Error , Cross Validation Techniques ,

"Unsupervised Image Region of Interest Detection using Discrete Cosine " by Abass Olaode, Golshah Naghdy et al.

Spatial domain analysis of images has been recognised as an important task in image retrieval and computer vision, however, its application in the determination of image Region of Interests often require the inclusion of supervised training and similarity computations, both of which can be difficult to implement during the modelling of the images in a large number of images. however, unsupervised image Region of Interest determination via Frequency domain analysis can be implemented without training or similarity computation therefore it is more suitable for the modelling of images in a large set. This paper demonstrates image frequency domain analysis via Discrete Cosine Transform and presents its application in the unsupervised determination of Region of Interests for the elimination of the spatial incoherency often associated with the Bag-of-Visual Word Modelling. ....

Discrete Cosine Transform , Bag Of Visual Word , Bag Of Visual Words , Discrete Cosine Transform , Frequency Domain Analysis , Region Of Interest , Patial Domain Analysis , Nsupervised Image Classification ,

"Joint Message-Passing-Based Bidirectional Channel Estimation and Equal" by Guang Yang, Qinghua Guo et al.

Acquiring accurate channel state information and mitigating severe intersymbol interference are challenging for underwater acoustic communications with moving transceivers due to the rapid changes of the underwater acoustic channels. In this work, we address the issue using a superimposed training (ST) scheme with a powerful channel estimation method. Different from the conventional time-multiplexed training, training sequences with a small power are superimposed with symbol sequences. The training signals are transmitted over all time, leading to enhanced tracking capability to deal with time-varying channels at the cost of only a small power loss. To realize this, based on the belief propagation, we develop a message-passing-based bidirectional channel estimation (BCE) algorithm, where all messages are Gaussian, enabling efficient implementation. In particular, the channel correlations are fully exploited through a forward recursion and a backward recursion, thereby achieving accurat ....

Jiaozhou Bay , Elief Propagation , Idirectional Channel Estimation , Channel Estimation , Frequency Domain Analysis , Ow Complexity Frequency Domain Equalization , Atching Pursuit Algorithms , Uperimposed Training , Ime Varying Underwater Acoustic Channel , Underwater Acoustics ,