arXiv research

This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper…

Method estimates parameters of complex nonlinear systems.

We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We show that universal consistency of Empirical Risk Minimiza…

Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.

We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We also show that, under some asumptions, universal consisten…

Bayesian optimization reduces RNN architecture search time.

The paper bounds the mean absolute error in DNN vector-to-vector regression.

We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We prove the existence of an optimal MAPE model and we show the universal consistency of Empirical Risk Minimization based on the MAPE. We also show that finding the best model under…

The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machin…

Near-optimal algorithms for mean estimation and linear regression with Gaussian covariates and Huber contamination.

This paper presents an out-of-sample prediction comparison between major machine learning models and the structural econometric model. Over the past decade, machine learning has established itself as a powerful tool in many prediction applications, but this approach is still not widely adopted in empirical economic stu…

The paper explores MAE as a loss function for DNN vector-to-vector regression, proving its advantages over MSE.

Study loop corrections in random feature models affecting training and test errors.

Study on LMMSE estimation with model mismatch, quantifying MSE trade-offs.

In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approac…

Optimizes shortfall risk using gradient-based methods.

The logcosh loss function helps neural networks learn set-valued functions better.

Optimal stock price prediction model using recurrent neural networks with RMSprop optimizer.

Random walks and polygons are used to model polymers. In this paper we consider the extension of writhe, self-linking number and linking number to open chains. We then study the average writhe, self-linking and linking number of random walks and polygons over the space of configurations as a function of their length. W…

Study forecasts Bitcoin prices using ML algorithms.

Paper introduces MADL loss function for better AIS model optimization.

LALR adapts learning rate for faster convergence in regression and neural nets.

Quantum algorithm estimates mean with sub-Gaussian error.

Paper proposes a new metric to evaluate survival models, especially for censored data.

Models predict probabilities of causation from limited data.

Optimizes sample reweighting to match laws under covariate shift using Wasserstein distance.

This paper compares ML models for predicting COVID-19 trends.

Study improves prediction accuracy and uncertainty for mobile sensor data using randomized neural networks.

The paper proposes a method to improve forecast combination accuracy using portfolio theory.

This paper achieves optimal regret bounds for locally private linear contextual bandit.

In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far. Consequently, we have two key findings: (1) Mean Absolute Error (MAE) D…

Neural network training process takes long time when the size of training data is huge, without the large set of training values the neural network is unable to learn features. This dilemma between time and size of data is often solved using fast GPUs, but we present a better solution for a subset of those problems. To…

The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…

Reconstruction of a function from noisy data is often formulated as a regularized optimization problem over an infinite-dimensional reproducing kernel Hilbert space (RKHS). The solution describes the observed data and has a small RKHS norm. When the data fit is measured using a quadratic loss, this estimator has a know…

LU-Net improves cardiac segmentation accuracy and robustness.

Study uses machine learning to predict soil organic carbon content in northern Iran.

Model predicts viscosity of multicomponent systems efficiently.

The purpose of this research is to apply technical analysis of Sutte Indicator in stock trading which will assist in the investment decision making process i.e. buying or selling shares. This research takes data of "A" on the Indonesia Stock Exchange(IDX or BEI) 29 November 2006 until 20 September 2016 period. To see t…

Paper shows MoM is optimal under adversarial contamination for certain distributions.

We consider statistical and algorithmic aspects of solving large-scale least-squares (LS) problems using randomized sketching algorithms. Prior results show that, from an \emph{algorithmic perspective}, when using sketching matrices constructed from random projections and leverage-score sampling, if the number of sampl…

Estimates sample size for subgroup analysis in randomized experiments.

Proposes a new loss function for learning with noisy labels.

Divide-and-conquer is a general strategy to deal with large scale problems. It is typically applied to generate ensemble instances, which potentially limits the problem size it can handle. Additionally, the data are often divided by random sampling which may be suboptimal. To address these concerns, we propose the $DC^…

Develops asymptotic analysis for RandNLA sampling estimators in least-squares problems.

Study evaluates 41 ML models for Bitcoin trading performance.

New model identifies patient-specific disease root causes.

A novel SVR parameter optimization method using GSA outperforms other meta-heuristics in stock market forecasting.

New framework reduces private mean estimation error with optimal efficiency.