Improved estimation of VAR-Moving Average models using GLS.
problem Estimating VAR-Moving Average models efficiently.
method Use generalized least squares (GLS) for each fixed moving average polynomial.
result Likelihood function format similar to VAR models, allowing maximum likelihood estimation.
This study uses moving average cluster entropy to analyze financial market dynamics.
problem Understanding long-range dependence in financial markets.
method Moving average cluster entropy approach applied to ARFIMA and FBM processes.
result Long-range positive correlation in financial markets is linked to the cluster entropy behavior.
The paper uses Bayesian methods to infer hidden processes with unknown parameters.
problem Estimating hidden processes from noisy observations with unknown parameters.
method Variational Bayesian inference with autoregressive moving average (ARMA) and vector autoregressive (VAR) models, combined with sequential Monte Carlo (SMC) and importance sampling resampling (SISR).
result The proposed inference method accurately estimates hidden states from non-linear noisy observations.
Paper develops online learning algorithms for quaternion ARMA models.
problem Adaptive learning for autoregressive moving average (ARMA) models in quaternion domain.
method Transformed learning problem into full information optimization task, solved using gradient descent and Newton's method.
result Online algorithms achieve asymptotic performance approaching best ARMA model.
We introduce an autoregressive-type model with self-modulation effects for a foreign exchange rate by separating the foreign exchange rate into a moving average rate and an uncorrelated noise. From this model we indicate that traders are mainly using strategies with weighted feedbacks of the past rates in the exchange …
WAVE improves time series forecasting by integrating AR and MA components.
problem Time series forecasting challenges.
method WAVE attention mechanism with AR and MA components.
result WAVE attention consistently improves TSF performance.
Long short-term memory network outperforms seasonal model in JSE Top 40 forecasting.
problem Comparing neural network performance to traditional models in financial forecasting.
method Used long short-term memory network for JSE Top 40 return data forecasting.
result Long short-term memory network outperforms seasonal model in forecasting.
Neural moving average model speeds up state space model inference for time series data.
problem Efficiently scaling approximate Bayesian inference for time series data.
method Proposes a novel generative model (neural moving average model) for latent temporal states in state space models.
result Achieves accurate parameter estimation in a short time for various models.
ARMA nets expand receptive fields for dense prediction tasks.
problem Global information in dense prediction problems is challenging for traditional convolutional layers.
method ARMA layers with adjustable autoregressive coefficients replace traditional convolutions.
result ARMA networks improve dense prediction tasks including video prediction and semantic segmentation.
We derive an explicit formula for likelihood function for Gaussian VARMA model conditioned on initial observables where the moving-average (MA) coefficients are scalar. For fixed MA coefficients the likelihood function is optimized in the autoregressive variables Φ's by a closed form formula generalizing regression c…
Optimizes prediction error method for time-varying models.
problem Achieving optimal prediction error rates for time-varying models.
method Nonlinear least squares method for time-varying parametric models.
result First rate-optimal non-asymptotic analysis for time-varying models.
Behavior cloning training instabilities amplified by SGD noise over long horizons.
problem Training instabilities in behavior cloning with deep neural networks.
method Empirical dissection of minibatch SGD updates and their effects on long-horizon rewards.
result Exponential moving average (EMA) of iterates effectively mitigates gradient variance amplification (GVA).
Automatically learns summary features from time series data for likelihood-free inference.
problem Necessity of hand-tailored summary features for time series data in likelihood-free inference.
method Data-driven approach to automatically learn summary features.
result Learning summary features from data can outperform hand-crafted values in likelihood-free inference.
Improved LSTM and ARIMA model for traffic flow forecasting.
problem Poor stability, high data requirements, and adaptability issues in existing traffic flow prediction methods.
method Combination prediction method based on improved LSTM and ARIMA models.
result The SDLSTM-ARIMA model achieves higher accuracy in traffic flow prediction.
Paper estimates non-causal graphical models using covariance extension and transportation distance.
problem Estimating non-causal graphical models with smoothing relations.
method Proposes a covariance extension problem and uses transportation distance to minimize error with white noise.
result Solution is a double-sided autoregressive non-causal graphical model.
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions,…
Time series analysis is a key component of machine learning, with applications in various fields.
problem Time series analysis in machine learning
method Basic concepts, classical statistical models, modern machine learning approaches
result Machine learning techniques for time series analysis
New methods for estimating ARMA and GARCH models with stable noise.
problem Estimating parameters of ARMA and GARCH models with stable noise.
method Modified Hannan-Rissanen Method and Modified Empirical Characteristic Function for estimation.
result Efficiency, accuracy, and simplicity of proposed methods demonstrated through simulation.
The paper highlights the importance of model discrepancy in cardiac simulations.
problem Uncertainty in model structure and equations affects predictions.
method The authors use Gaussian processes and autoregressive-moving-average models to account for model discrepancy.
result Different methods to account for model discrepancy have advantages and shortcomings.
Study finds RNNs predict STBG better than ARIMA, useful for diabetes patients.
problem Improving short-term blood glucose prediction for diabetes management.
method Investigated Recurrent Neural Networks (RNNs) and compared them to ARIMA for STBG prediction.
result Population-based RNN model outperforms ARIMA across various prediction horizons.
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Auto…
Study models live cattle futures prices in Brazil.
problem Estimate futures prices for better risk management.
method Holt-Winters, ARIMA, GARMA models.
result GARMA(2,1) model best predicts BGI prices.
Study compares local and global models for hierarchical forecasting accuracy.
problem Challenges in hierarchical time series forecasting, especially in accuracy and information utilisation.
method Developed and evaluated local and global forecasting models (GFMs) to exploit cross-series and cross-hierarchies information.
result Global Forecasting Models (GFMs) outperform local models in hierarchical forecasting accuracy and computational efficiency.
In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to…
New model prices options with complex market data structures.
problem Complex market data structures in option pricing.
method Compound CARMA(p,q)-Hawkes model.
result Model can replicate volatility smile in financial markets.
Deep learning models outperform traditional methods in stock price prediction.
problem Improving stock price prediction accuracy using deep learning.
method Comparative analysis of deep learning models (LSTM, GRU) and traditional methods (ARIMA, ARMA) on historical data.
result Deep learning models, particularly LSTM, outperform traditional methods in predicting stock prices across different time horizons.
Deep learning predicts road GHG emissions with speed, density, and past ERs.
problem Predicting GHG emissions from road networks to mitigate environmental impact.
method Developed a deep learning framework using LSTM networks with exogenous variables.
result LSTM with speed, density, GHG ER, and in-links speed from previous minutes performs best.
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The mo…
ARMA cell simplifies neural autoregressive modeling for time series.
problem Complex RNN cells are not always necessary and can be inferior.
method Introduces ARMA cell, a simpler, modular approach for neural time series modeling.
result The ARMA cell is competitive with popular alternatives in performance.
Combines spline interpolation and ARIMA for stock market forecasting.
problem Limited predictive performance of ARIMA in noisy data.
method Integrates cubic spline interpolation and ARIMA for time series forecasting.
result Demonstrates guidance for short-term stock market forecasting.
Paper uses LSTM neural networks to forecast commodity prices.
problem Forecasting accuracy of traditional methods like ARIMA.
method Long Short-Term Memory (LSTM) neural networks complement traditional methods.
result Forecast averaging of LSTM and ARIMA models improves forecast accuracy.
Explains the difference between EMA and moving EMA, focusing on market trend indicators.
problem Understanding the difference between exponential moving average and moving exponential average.
method Explains the mathematical tools and definitions of trend indicators.
result Discusses the properties of the MACD indicator and its use in market trend analysis.
New moving average adapts weight dynamically based on polynomial and wavefunction.
problem Lagging traditional moving averages in adjusting to changes in data.
method Develops a moving average with weight as a polynomial of a wavefunction from an eigenproblem.
result Immediate 'switch' without lag, adapting to changes in data.
Generative model creates realistic dance poses from music.
problem Generating human-like dance poses from music.
method Music feature encoder, pose generator, music genre classifier integrated.
result Generative autoregressive model synthesizes dance sequences up to 5,000 frames.
In this paper we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving-average (SARMA) models under the assumption that the errors are uncorrelated but not necessarily independent.We relax the standard independence assumption on the error term in order to extend the range …
New method for estimating and testing impulse responses in high-dimensional VAR systems.
problem Statistical inference for impulse responses in sparse, high-dimensional vector autoregressions.
method Local projection equations and de-sparsified estimators combined with a non-regularized contemporaneous impact matrix.
result Valid inference procedures for structural impulse responses in high-dimensional systems.
New method combines long-memory reservoirs for accurate dengue forecasting from short data.
problem Accurate dengue forecasting from short, noisy, non-stationary, and nonlinear data.
method Fractional ESN and Wavelet ESN frameworks integrating long-term memory.
result fESN and wESN outperform baselines in multiple dengue datasets and forecasting horizons.
There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. The multifractal detrended cross-correlation analysis (MF-DCCA) approaches can be used to quantify such cross-correlations, such as the MF-DCCA based on detrend…
New method for identifying autoregressive systems on manifolds.
problem Identifying autoregressive systems on Stiefel and Grassmann manifolds.
method Defining parameters as orthogonal group elements, averaging over observations, conjugate gradient descent on manifolds.
result System parameters can be estimated efficiently using the proposed algorithm.
PARNN improves ARNN with ARIMA feedback for accurate long-range forecasting.
problem Accurate long-range forecasting of complex time series data.
method Improves ARNN using ARIMA feedback, providing uncertainty quantification.
result PARNN outperforms state-of-the-art forecasters across various horizons.
We derive generalization error bounds for traditional time-series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These non-asymptotic bounds need only weak assumptions on the data-generating…
Paper proposes a new sparse VAR model for high-dimensional time series.
problem Non-identifiability, computational intractability, and difficulty of interpretation for high-dimensional time series.
method Sparse infinite-order VAR model with ℓ1-regularized estimation methods. result Greater statistical efficiency and interpretability achieved with little loss of temporal information.
Proposes a method for forecasting time series with multiple seasonality.
problem Forecasting time series with both short-term and long-term seasonality is challenging.
method Two-stage method: first generalizes ARMA model for multiple seasonality, second selects lag order.
result Method outperforms `Facebook Prophet` model in predictive performance.
A new model prices assets considering market microstructure effects.
problem Including market microstructure effects in dynamic asset pricing.
method Discrete binary tree model with history-dependent underlying security prices.
result The model preserves historical price dynamics and is market-complete, arbitrage-free.
ARIMA model shows promise for short-term Bitcoin price prediction but fails for long-term predictions.
problem Predicting Bitcoin's future price using ARIMA model.
method Traditional ARIMA model applied to Bitcoin price time series.
result ARIMA model performs well for short-term predictions but poorly for long-term predictions due to its inability to capture sharp price fluctuations.
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication…
In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient ρDMCA(λ) with a moving average window length λ. We analytically show that the coefficient…