Prediction intervals for the vector singular spectrum analysis forecasting algorithm in a median‐based singular spectrum analysis

Mahmoudvand, Rahim; Rodrigues, Paulo Canas

doi:10.1002/cmm4.1080

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Mahmoudvand and Canas Rodrigues (2021) developed prediction bands for the SSA vector forecasting algorithm that are based on the median of predictions rather than the average that is standard in many bootstrap methods. In addition, the concept of functional quantiles was developed in López‐Pintado and Romo (2009) with the goal of establishing centrality of functional observations.…”

Section: Fssa Forecastingmentioning

confidence: 99%

Functional time series forecasting: Functional singular spectrum analysis approaches

Trinka,

Haghbin,

Lin Shang

et al. 2023

Stat

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We introduce two novel nonparametric forecasting methods designed for functional time series (FTS), namely, functional singular spectrum analysis (FSSA) recurrent and vector forecasting. Our algorithms rely on extracted signals obtained from the FSSA method and innovative recurrence relations to make predictions. These techniques are model‐free, capable of predicting nonstationary FTS and utilize a computational approach for parameter selection. We also employ a bootstrap algorithm to assess the goodness‐of‐prediction. Through comprehensive evaluations on both simulated and real‐world climate data, we showcase the effectiveness of our techniques compared to various parametric and nonparametric approaches for forecasting nonstationary stochastic processes. Furthermore, we have implemented these methods in the Rfssa R package and developed a shiny web application for interactive exploration of the results.

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Section: Fssa Forecastingmentioning

confidence: 99%

Functional time series forecasting: Functional singular spectrum analysis approaches

Trinka,

Haghbin,

Lin Shang

et al. 2023

Stat

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“…Two main algorithms for out-of-the-sample forecasting in the context of SSA are available: the recurrent SSA forecasting algorithm [23,40,41], and the vector SSA forecasting algorithm [23,42,43]. Here we will be interested in the recurrent SSA forecasting algorithm, which is briefly described below.…”

Section: Third Stage: Forecastingmentioning

confidence: 99%

Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks

Rodrigues

Awe

Pimentel

et al. 2020

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A proper understanding and analysis of suitable models involved in forecasting currency exchange rates dynamics is essential to provide reliable information about the economy. This paper deals with model fit and model forecasting of eight time series of historical data about currency exchange rate considering the United States dollar as reference. The time series techniques: classical autoregressive integrated moving average model, the non-parametric univariate and multivariate singular spectrum analysis (SSA), artificial neural network (ANN) algorithms, and a recent prominent hybrid method that combines SSA and ANN, are considered and their performance compared in terms of model fit and model forecasting. Moreover, specific methodological and computational adaptations were conducted to allow for these analyses and comparisons.

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“…Its main aim is to decompose the original time series into a set of components that can be interpreted as trend components, seasonal components, and noise components [ 3 , 4 , 5 , 6 ]. SSA has proven both wide usefulness and applicability across many applications [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], being that its scope of application ranges from parameter estimation to time series filtering, synchronization analysis, and forecasting [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Rodrigues

Pimentel

Messala

et al. 2020

Entropy

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Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noise-free time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and non-parametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models.

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Prediction intervals for the vector singular spectrum analysis forecasting algorithm in a median‐based singular spectrum analysis

Cited by 4 publications

References 22 publications

Functional time series forecasting: Functional singular spectrum analysis approaches

Functional time series forecasting: Functional singular spectrum analysis approaches

Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

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