We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
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48 results for “autoregressive moving-average”
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.
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.
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 …
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.
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 '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.
In this article, we first propose the modified Hannan-Rissanen Method for estimating the parameters of the autoregressive moving average (ARMA) process with symmetric stable noise and symmetric stable generalized autoregressive conditional heteroskedastic (GARCH) noise. Next, we propose the modified empirical character…
Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a …
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.
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.
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).
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most applicable to models of independent data. We propose an extension to state space models of time series data based on a novel generative 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 in Machine Learningastro-ph.IM
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
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.
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.
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.
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.
Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning approach to devise variational Bayesian inference for such scenarios. In particular, a random process generated by the autoregressive moving…
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…
Application Research of Spline Interpolation and ARIMA in the Field of Stock Market Forecastingq-fin.ST
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.
Modelling risk for commodities in Brazil: An application to live cattle spot and futures pricesq-fin.ST
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.
In this paper, we address the problem of adaptive learning for autoregressive moving average (ARMA) model in the quaternion domain. By transforming the original learning problem into a full information optimization task without explicit noise terms, and then solving the optimization problem using the gradient descent a…
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…
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.
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.
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 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.
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.
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 -regularized estimation methods.
result Greater statistical efficiency and interpretability achieved with little loss of temporal information.
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…
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.
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…
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.
Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocksq-fin.ST
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.
Study analyzes climate impact on agricultural prices, offering insurance solutions.
problem Financial risk from climate-induced agricultural price volatility.
method Historical and future climate projections, EGARCH and SARIMAX models, Black-Scholes framework.
result Improved agricultural risk modeling and insurance mechanisms.
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.
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Datacs.LG
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…
We derive expressions for the predicitive information rate (PIR) for the class of autoregressive Gaussian processes AR(N), both in terms of the prediction coefficients and in terms of the power spectral density. The latter result suggests a duality between the PIR and the multi-information rate for processes with mutua…
Forecasting Short-term load using Econometrics time series model with T-student Distributionq-fin.ST
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
problem Accurate short-term load forecasting for optimizing electrical sources and protecting energy.
method Uses SARIMA-GARCH model with T-student Distribution to forecast electric load.
result The proposed model outperforms the ARIMA model with Normal Distribution.
It is generally accepted that many time series of practical interest exhibit strong dependence, i.e., long memory. For such series, the sample autocorrelations decay slowly and log-log periodogram plots indicate a straight-line relationship. This necessitates a class of models for describing such behavior. A popular cl…
Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregress…
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
We derive the price of a spread option based on two assets which follow a bivariate volatility modulated Volterra process dynamics. Such a price dynamics is particularly relevant in energy markets, modelling for example the spot price of power and gas. Volatility modulated Volterra processes are in general not semimart…
Introduces a new Hawkes model with CARMA(p,q) intensity to better model dependence structures.
problem Modeling dependence structures in time series data with realistic autocorrelation functions.
method Develops a Hawkes process with CARMA(p,q) intensity to capture more complex dependencies.
result The CARMA(p,q)-Hawkes model can reproduce more realistic dependence structures and is stationary and positive.