arXiv research

This paper enhances LSTM neural networks for multi-variable time series data, providing interpretable insights.

In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To …

In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To th…

We will prove that, for a $2$ or $3$ component $L$-space link, $HFL^-$ is completely determined by the multi-variable Alexander polynomial of all the sub-links of $L$, as well as the pairwise linking numbers of all the components of $L$. We will also give some restrictions on the multi-variable Alexander polynomial of …

Defines a new polynomial invariant for virtual links.

Updated polynomial for virtual tangles, compatible with decompositions.

New Alexander polynomial defined for transverse graphs.

The Conway potential function (CPF) for colored links is a convenient version of the multi-variable Alexander-Conway polynomial. We give a skein characterization of CPF, much simpler than the one by Murakami. In particular, Conway's `smoothing of crossings' is not in the axioms. The proof uses a reduction scheme in a t…

Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method uti…

MCP extends conformal prediction to vector-valued score functions without data splitting.

Paper compares ML models for fast power system contingency case identification.

UCB algorithms estimate uplifts in multi-variable reward systems.

In recent years, twisted Alexander polynomial has been playing an important role in low-dimensional topology. For Montesinos links, we develop an efficient method to compute the twisted Alexander polynomial associated to any linear representation. In particular, formulas for multi-variable Alexander polynomials of thes…

We show that link Floer homology detects the Thurston norm of a link complement. As an application, we show that the Thurston polytope of an alternating link is dual to the Newton polytope of its multi-variable Alexander polynomial. To illustrate these techniques, we also compute the Thurston polytopes of several speci…

The paper extends Pearson correlation to multi-variables, useful for noise measurement and feature selection.

Machine learning predicts TV show success based on factors like characters and direction.

We construct the Einstein equation for an invariant Riemannian metric on the exceptional full flag manifold $M=G_2/T$. By computing a Gröbner basis for a system of polynomials of multi-variables we prove that this manifold admits exactly two non-Kähler invariant Einstein metrics. Thus $G_2/T$ turns out to be the first …

In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable network-level responses. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While c…

This paper speeds up OCSSVM training using SMO.

In many regular cases, there exists a (properly defined) limit of iterations of a function in several real variables, and this limit satisfies the functional equation (1-z)f(x)=f(f(xz)(1-z)/z); here z is a scalar and x is a vector. This is a special case of a well-known translation equation. In this paper we present a …

There are more than eight hundred interest rates published in China bond market every day. Which are the benchmark interest rates that have broad influences on most interest rates is a major concern for economists. In this paper, multi-variable Granger causality test is developed and applied to construct a directed net…

Research tackles unequal length time series for classification.

The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable compar…

Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, $u_i$, can be detected and quantified by studying the correlations in the magnitude series $|u_i|$, i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact …

A mathematical paradox shows secant planes don't always form a tangent plane, but some analogies hold with a specific vector product.

Modeling regime shifts in co-evolving time series with interactions and time-dependency.

MDF represents time series motifs as images for improved classification.

We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes fro…

Paper proposes a robust time series classification method using ResNet and Recurrence Plots.

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through s…

Overview of high-dimensional time series regression methods.

Automatically extracts features from time series data for improved forecasting.

New method uses Transformers for flu forecasting.

Improved prediction of hierarchical time series using structured regularization.

Introduces a new benchmark for time series extrinsic regression.

Few-shot learning improves time-series forecasting with limited data.

Meta-learning for Koopman spectral analysis with short time-series data.

Transformers improve time series modeling by capturing long-range dependencies.

Archive of 20 time series datasets for forecasting evaluation.

Multidimensional time series are sequences of real valued vectors. They occur in different areas, for example handwritten characters, GPS tracking, and gestures of modern virtual reality motion controllers. Within these areas, a common task is to search for similar time series. Dynamic Time Warping (DTW) is a common di…

theft package simplifies feature extraction for time series analysis in R.

SOEM clusters time series data with improved accuracy.

Dilated CNN improves multivariate time series classification.

Method summarizes and predicts time series data for COVID-19 cases and deaths.

Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; w…

AR model forecasts partially observed dynamical time series by estimating evolution function and imputing missing variables.

The paper analyzes online learning of smooth functions in both single and multi-variable settings.

New deep probabilistic model handles missing data in time series forecasting.