A Functional Time Series Analysis of Forward Curves Derived from Commodity Futures

Horváth, Lajos; Liu, Zhenya; Rice, Gregory; Wang, Shixuan

doi:10.2139/ssrn.3227025

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…FAR(1) was later extended to FAR(p), under which the order p can be determined via Kokoszka and Reimherr's (2013) hypothesis testing. Horváth et al (2020) compared the forecasting performance between FAR(1), FAR(p), and functional seasonal autoregressive models of Chen et al (2019b).…”

Section: Functional Time Series Models 18mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

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Section: Functional Time Series Models 18mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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show abstract

“…Reference [37] proposed a dimension reduction technique to model the functional ARMA (FARMA) model with an application to traffic data. Among the existing literature, the FTS models have been employed to forecast intraday trajectories; see, for example, Shang [38,39] and Horváth et al [40].…”

Section: Functional Data Analysismentioning

confidence: 99%

Functional Data Approach for Short-Term Electricity Demand Forecasting

Shah

Jan

Ali

2022

Mathematical Problems in Engineering

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In today’s liberalized electricity markets, modeling and forecasting electricity demand data are highly important for the effective management of the power system. However, electricity demand forecasting is a challenging task due to the specific features it exhibits. These features include the presence of extreme values, spikes or jumps, multiple periodicities, long trend, and bank holiday effect. In addition, the forecasts are required for a complete day as electricity demand is decided a day before the physical delivery. Therefore, this study aimed to investigate the forecasting performance of models based on functional data analysis, a relatively less explored area in energy research. To this end, the demand time series is first treated for the extreme values. The filtered series is then divided into deterministic and stochastic components. The generalized additive modeling technique is used to model the deterministic component, whereas functional autoregressive (FAR), FAR with exogenous variable (FARX), and classical univariate AR models are used to model and forecast the stochastic component. Data from the Nord Pool electricity market are used, and the one-day-ahead out-of-sample forecast obtained for a whole year is evaluated using different forecasting accuracy measures. The results indicate that the functional modeling approach produces superior forecasting results, while FARX outperforms FAR and classical AR models. More specifically, for the NP electricity demand, FARX produces a MAPE value of 2.74, whereas 6.27 and 9.73 values of MAPE are obtained for FAR and AR models, respectively.

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“…While functional data are now quite well studied, a relatively new field of research is emerging which aims to analyze the time-varying characteristics of such functional data. Functional time series data often arise in many important problems, such as analyzing forward curves derived from commodity futures [15], daily patterns of geophysical and environmental data [30], demographic quantities, such as age-specific fertility or mortality rates studied over time [31], and neurophysiological data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), recorded at various locations in the brain [12,32]. For example, NASA records surface temperatures for more than 5000 locations, and these readings are used to identify yearly temperature anomalies, which are crucial in studying global warming patterns [18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Frequency Band Analysis for Functional Time Series

Bagchi,

Bruce

2021

Preprint

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The frequency-domain properties of nonstationary functional time series often contain valuable information. These properties are characterized through its time-varying power spectrum. Practitioners seeking low-dimensional summary measures of the power spectrum often partition frequencies into bands and create collapsed measures of power within bands. However, standard frequency bands have largely been developed through manual inspection of time series data and may not adequately summarize power spectra. In this article, we propose a framework for adaptive frequency band estimation of nonstationary functional time series that optimally summarizes the time-varying dynamics of the series. We develop a scan statistic and search algorithm to detect changes in the frequency domain. We establish theoretical properties of this framework and develop a computationallyefficient implementation. The validity of our method is also justified through numerous simulation studies and an application to analyzing electroencephalogram data in participants alternating between eyes open and eyes closed conditions. MSC 2020 subject classifications. 62M15, 62G20.

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A Functional Time Series Analysis of Forward Curves Derived from Commodity Futures

Cited by 3 publications

References 53 publications

Forecasting: theory and practice

Forecasting: theory and practice

Functional Data Approach for Short-Term Electricity Demand Forecasting

Adaptive Frequency Band Analysis for Functional Time Series

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