In today's blog post, we shall look into time series analysis using R package – forecast. Objective of the post will be explaining the different methods available in forecast package which can be applied while dealing with time series analysis/forecasting. A time series is a collection of observations of well-defined data items obtained through repeated measurements over time. For example, measuring the value of retail sales each month of the year would comprise a time series. My data set contains data of Sales of CARS from Jan-2008 to Dec 2013.

Continuing analysis from last year: Top 20 Python Machine Learning Open Source Projects, this year KDnuggets bring you latest top 20 Python Machine Learning Open Source Projects on Github. Strangely, some of the most active projects of last year have become stagnant and also some lost their position from top 20 (considering contributions and commits), whereas new 13 projects have entered into top 20. Scikit-learn is simple and efficient tools for data mining and data analysis, accessible to everybody, and reusable in various context, built on NumPy, SciPy, and matplotlib, open source, commercially usable – BSD license. Commits: 21486, Contributors: 736, Github URL: Scikit-learn Tensorflow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

More than fifty years of research in molecular biology have demonstrated that the ability of small and large molecules to interact with one another and propagate the cellular processes in the living cell lies in the ability of these molecules to assume and switch between specific structures under physiological conditions. Elucidating biomolecular structure and dynamics at equilibrium is therefore fundamental to furthering our understanding of biological function, molecular mechanisms in the cell, our own biology, disease, and disease treatments. By now, there is a wealth of methods designed to elucidate biomolecular structure and dynamics contributed from diverse scientific communities. In this survey, we focus on recent methods contributed from the Robotics community that promise to address outstanding challenges regarding the disparate length and time scales that characterize dynamic molecular processes in the cell. In particular, we survey robotics-inspired methods designed to obtain efficient representations of structure spaces of molecules in isolation or in assemblies for the purpose of characterizing equilibrium structure and dynamics. While an exhaustive review is an impossible endeavor, this survey balances the description of important algorithmic contributions with a critical discussion of outstanding computational challenges. The objective is to spur further research to address outstanding challenges in modeling equilibrium biomolecular structure and dynamics.

We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification. We propose to adopt kernel machines and employ graph kernels that define a kernel dot product between two graphs. This enables us to take advantage of spatio-temporal information to capture the dynamics of the brain network, as opposed to aggregating them in the spatial or temporal dimension. In addition to the conventional similarity graphs, we explore the use of L1 graph using sparse coding, and the persistent homology of time delay embeddings, in the proposed pipeline for ASD classification. In our experiments on two datasets from the ABIDE collection, we demonstrate a consistent and significant advantage in using graph kernels over traditional linear or non linear kernels for a variety of time series features.

Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings of these words are usually a mixture of multiple meanings. In this paper, we acknowledge multiple identities of the same word in different contexts and learn the \textbf{identity-sensitive} word embeddings. Based on an identity-labeled text corpora, a heterogeneous network of words and word identities is constructed to model different-levels of word co-occurrences. The heterogeneous network is further embedded into a low-dimensional space through a principled network embedding approach, through which we are able to obtain the embeddings of words and the embeddings of word identities. We study three different types of word identities including topics, sentiments and categories. Experimental results on real-world data sets show that the identity-sensitive word embeddings learned by our approach indeed capture different meanings of words and outperforms competitive methods on tasks including text classification and word similarity computation.

Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli - Sodium Chloride (NaCl), Sulphuric Acid (H2SO4) and Ozone (O3). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

Phase synchronisation in multichannel EEG is known as the manifestation of functional brain connectivity. Traditional phase synchronisation studies are mostly based on time average synchrony measures hence do not preserve the temporal evolution of the phase difference. Here we propose a new method to show the existence of a small set of unique phase synchronised patterns or "states" in multi-channel EEG recordings, each "state" being stable of the order of ms, from typical and pathological subjects during face perception tasks. The proposed methodology bridges the concepts of EEG microstates and phase synchronisation in time and frequency domain respectively. The analysis is reported for four groups of children including typical, Autism Spectrum Disorder (ASD), low and high anxiety subjects - a total of 44 subjects. In all cases, we observe consistent existence of these states - termed as synchrostates - within specific cognition related frequency bands (beta and gamma bands), though the topographies of these synchrostates differ for different subject groups with different pathological conditions. The inter-synchrostate switching follows a well-defined sequence capturing the underlying inter-electrode phase relation dynamics in stimulus- and person-centric manner. Our study is motivated from the well-known EEG microstate exhibiting stable potential maps over the scalp. However, here we report a similar observation of quasi-stable phase synchronised states in multichannel EEG. The existence of the synchrostates coupled with their unique switching sequence characteristics could be considered as a potentially new field over contemporary EEG phase synchronisation studies.

Over the last years we have witnessed a rapid increase in the availability of GPSreceiving devices, such as smart phones or car navigation systems. The devices generate vast amounts of temporal positioning data that have been proven invaluable in various applications, from traffic management (Kühne et al., 2003) and route planning (Gonzalez et al., 2007; Li et al., 2011; Kowalska et al., 2015) to inferring personal movement signatures (Liao et al., 2006). Critical to the utility of GPS data is their accuracy. The data suffer from measurement errors caused by technical limitations of GPS receivers and sampling errors caused by their receiving rates. When digital maps are available, it is common practice to improve the accuracy of the data by aligning GPS points with the road network. The process is known as map-matching. Most map-matching algorithms align GPS trajectories with the road network by considering positions of each GPS point, either in isolation or in relation to other GPS points in the same trajectory.

We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to per- form non-iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet-based signal denoising methods. We theoretically justify our approach, based on the fact that for smooth manifolds the coordinate information energy is localized in the low spectral graph wavelet sub-bands, while the noise affects all frequency bands in a similar way. Experimental results show that our proposed manifold frequency denoising (MFD) approach significantly outperforms the state of the art denoising meth- ods, and is robust to a wide range of parameter selections, e.g., the choice of k nearest neighbor connectivity of the graph.

Consider a general practice physician treating a patient with unusual or ambiguous symptoms. The general practitioner often does not have the capability to confidently diagnose such an ailment. The doctor is faced with a difficult choice: either commit to a potentially erroneous diagnosis and act on it, which can have catastrophic consequences; orabstain from any such diagnosis and refer the patient to a specialist or hospital instead, which is safer but will certainly cost extra time and resources. Such a situation motivates the study of classifiers which are able not only to form a hypothesis about the correct classification, but also abstain entirely from making a prediction. A sufficiently self-aware abstaining classifier might abstain on examples on which it is most unsure about the label, lowering the average prediction error it suffers when it does commit to a prediction. Like the doctor in the example, however, there is typically no use in abstaining on all data, so the amount of overall abstaining is somehow restricted. The classifier must allocate limited abstentions where they will most reduce error. There has been much historical work in decision theory and machine learning on learning such abstaining classifiers (e.g.