Deterministic or Stochastic Trend

Stadnytska, Tetiana

doi:10.1027/1614-2241/a000009

Cited by 18 publications

(4 citation statements)

References 42 publications

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“…When the measurement data is sequentially collected and may be randomly varying, FD method is recommanded for data pre-processing. Meanwhile, implementing FD method to initially manipulate the raw data can avoid the stochastic trend problem [29]. The first difference method at time t can be defined as:…”

Section: B Flow Parameters Measurementmentioning

confidence: 99%

Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

Wang

Zhang

et al. 2023

IEEE Sensors J.

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Accurate multiphase flow measurement is vital in monitoring and optimizing various production processes. Deep learning has as of late arose as a promising approach for assessing multiphase flowrate dependent on various customary flow meters. In this paper, we propose a multi-modal sensor and Temporal Convolution Network (TCN) based method to predict the volumetric flowrate of oil/gas two-phase flows. The volumetric flowrates of the liquid and gas phase vary from 0.96 -6.13 m 3 /h and 5.5 -121.2 m 3 /h, respectively. The multi-modal sequential sensing data are simultaneously collected from a Venturi tube and a dual-plane Electrical Capacitance Tomography (ECT) sensor in a pilot-scale multiphase phase flow facility. The reference data are derived from the single-phase flowmeters. Z-score and First-Difference (FD) data pre-processing methods are employed to manipulate the collected instantaneous time series multi-modal sensing data. The pre-processed data are utilized for training the TCN model. Experimental results reveal that the TCN model can effectively predict the multiphase flowrate based on the multi-modal sensing data.The results provide guidance on data pre-processing methods for multiphase flowrate estimation and demonstrate the effectiveness of combining multi-modal sensors and TCN for multiphase flowrate prediction under complex flow conditions.

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Section: B Flow Parameters Measurementmentioning

confidence: 99%

Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

Wang

Zhang

et al. 2023

IEEE Sensors J.

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“…For sequential modeling to be undertaken, the time series must be stable, with constant mean, variance, and covariance over time. To evaluate this, the Dickey-Fuller test of stationarity was used (Charemza & Syczewska, 1998;Stadnytska, 2010). The next step in selecting an ARIMA model for the time series is to determine the need for autoregression (AR) and moving average (MA) terms to accurately correct for any autocorrelation present in the differenced series.…”

Section: Stationary Time Series Scale and Trend Analysismentioning

confidence: 99%

Forecasting occurrence of palm weevil Rhynchophorus palmarum L. (Coleoptera, Curculionidae) using Autoregressive Integrated Moving Average modeling

Pacheco-Sánchez,

Guamani-Quimis,

Ewerling da Rosa

et al. 2023

Sci. agropecu.

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Oil palm (Elaeis guineensis L.) is a crucial crop in Ecuador, considerably affected by black palm weevil Rhynchophorus palmarum L. (Coleoptera: Curculionidae) for several years. Despite its importance, the behavior of the black weevil in Ecuador is not well comprehended presently. Therefore, this study aimed to predict infestation patterns of the black palm weevil in Ecuador using a mathematical model based on monitoring data. Data on the number of insects per trap from a commercial oil palm farm in Quinindé, Ecuador, was collected every two weeks for five years (2016-2020) and analyzed using the Classical Fourier (CF) spectrum and the Dickey-Fuller test to determine seasonality. The trend component of the data dropped from 16.33 in January 2017 to 11.96 in January 2019, with a fluctuation ranging from -0.11 to 2.50 observed for the entire data set. The results obtained after fitting the model ranged from -0.11 to 3.19, with a maximum of 5.30. The augmented Dickey-Fuller (ADF) test for the black weevil time series yielded a result of -5.60 (P<0.01). The partial autocorrelation ranged from -0.35 to 0.1. Based on our model, we projected the occurrence of black palm weevil from 2021 to 2024, with a fluctuation in the number of insects per trap ranging from 12.68 in January 2021 to 13.023 in November 2023. This model can be used to predict future insect occurrences in Ecuador, providing valuable insights into the behavior of the black weevil and using it for effective development control measures for this pest.

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“…Another prominent approach is the "augmented Dickey-Fuller test" (ADF) (Gujarati, 2003). But, according to Stadnytska (2010), ADF inflates the actual significance level, and a correction for such effect leads to moderate statistical powers. Also, a method conventionally used for testing the null hypothesis of deterministic stationary trend, versus the alternative of a stochastic trend, is the test proposed by Hobijn, Franses, and Ooms (2004), and generalized by Kwiatkowski, Phillips, Schmidt, and Shin (1992), called KPSS, which has been extensively used in many fields (Chen & Pun, 2019;Montasser, 2015).…”

Section: Overviewmentioning

confidence: 99%

Monte Carlo Test for Stochastic Trend in Space State Models for the Location-Scale Family

Silva

Ernesto

Oliveira

et al. 2021

Brazilian Review of Econometrics

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In space state models for time series, a key point is the decision between modeling the trend of non-stationary processes through a deterministic or a stochastic term. The present paper introduces a Monte Carlo hypothesis test procedure to guide in such a decision. The method works for any time series distribution belonging to the location-scale family. The proposed method provides an alpha-level test for any time series of length greater than 3 and it does not demand assumptions on the distribution of the trend term when it is actually stochastic.

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Deterministic or Stochastic Trend

Cited by 18 publications

References 42 publications

Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

Forecasting occurrence of palm weevil Rhynchophorus palmarum L. (Coleoptera, Curculionidae) using Autoregressive Integrated Moving Average modeling

Monte Carlo Test for Stochastic Trend in Space State Models for the Location-Scale Family

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