TSrepr: R package for time series representations

Blog about time series data mining in R.

Code used in the paper "Time Series Clustering via Community Detection in Networks"

COVID-19 spread shiny dashboard with a forecasting model, countries' trajectories graphs, and cluster analysis tools

Clustering using tslearn for Time Series Data.

Dynamic Time Warping (DTW) and related algorithms in Julia

Different deep learning architectures are implemented for time series classification and prediction purposes.

Clustering-based Forecasting Method for Individual End-consumer Electricity Consumption Using Smart Grid Data

PyIOmica (pyiomica) is a Python package for omics analyses.

A symbolic time series representation building Brownian bridges

Unsupervised ensemble learning methods for time series forecasting. Bootstrap aggregating (bagging) for double-seasonal time series forecasting and its ensembles.

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

A clustering algorithm that will perform clustering on each of a time-series of discrete datasets, and explicitly track the evolution of clusters over time.

Density-based clustering unsupervised ensemble learning methods for forecasting double seasonal time series

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify ''weak-edges'' that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.

Code for "Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest"

Clustering and segmentation of heterogeneous functional data (sequential data) with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm

Time Series Stream Clustering for London Smart Meter Dataset

Clustering Bike Share Toronto time series data to identify temporal behavioural motifs.

Clustering and segmentation of heteregeneous functional data (sequential data) by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm

Capstone project for Galvanize Data Science Immersive Program

Time Series Clustering as the pre-processing step for "The Closing Price of Sharia Shares in Indonesia" data

2018 UCR Time-Series Archive: Backward Compatibility, Missing Values, and Varying Lengths