Long Range Forecasting (From Crystal Ball to Computer).

Ward, R.H.; Armstrong, J. Scott

doi:10.2307/2988075

Cited by 72 publications

(6 citation statements)

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“…The CUF of wind power plants steadily increased from 31.8% in 2012 to 34.6% in 2021. The CUF of PV power plants was 24.5%, which is lower than other power plants [13]. This is because PV power plants have shorter operation times than other power plants.…”

Section: A Capacity Utility Factor(cuf)mentioning

confidence: 99%

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Quality Analysis of Photovoltaic System Using Descriptive Statistics of Power Performance Index

et al. 2023

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Section: A Capacity Utility Factor(cuf)mentioning

confidence: 99%

“…Unlike MAPE, sMAPE can be analyzed symmetrically with an upper limit of 200%. The mathematical expressions of MAPE and sMAPE are as follows [24] [25].…”

Section: Expected Model Validation Of Pv Systemmentioning

confidence: 99%

Quality Analysis of Photovoltaic System Using Descriptive Statistics of Power Performance Index

et al. 2023

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“…This definition slightly differs from the original definition of this measure by Armstrong (1985). First, the denominator is not divided by 2 to allow for an easier interpretation.…”

Section: Symmetric Mean Absolute Percentage Errormentioning

confidence: 99%

Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Schimohr

Doebler

Scheiner

2022

Geographical Analysis

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Regression models are commonly applied in the analysis of transportation data. This research aims at broadening the range of methods used for this task by modeling the spatial distribution of bike‐sharing trips in Cologne, Germany, applying both parametric regression models and a modified machine learning approach while incorporating measures to account for spatial autocorrelation. Independent variables included in the models consist of land use types, elements of the transport system and sociodemographic characteristics. Out of several regression models with different underlying distributions, a Tweedie generalized additive model is chosen by its values for AIC, RMSE, and sMAPE to be compared to an XGBoost model. To consider spatial relationships, spatial splines are included in the Tweedie model, while the estimations of the XGBoost model are modified using a geographically weighted regression. Both methods entail certain advantages: while XGBoost leads to far better values regarding RMSE and sMAPE and therefore to a better model fit, the Tweedie model allows an easier interpretation of the influence of the independent variables including spatial effects.

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“…In this case, there is no need to consider the causes or relationships underlying changes in the data. Common univariate time series forecasting methods include the moving average method (Armstrong, 1985), the exponential smoothing method (Fildes & Lusk, 1984), the Box-Jenkins method (Hill & Fildes, 1984), the ARARMA model (Meade & Smith, 1985), the Pandit-Wu method (Pandit & Wu, 1983), the intervention analysis model (Thury & Anderson, 1980), the state space model and the Bayesian forecasting method (Abraham & Ledolter, 1983). Instead, multivariate forecasting focuses on analyzing causal or correlation relationships between two or more variables.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Linkage Fluctuation in Time Series Data of Nickel Futures Price Index

Chen,

Huo,

Cao

2023

Journal of Business Economics and Management

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This paper explores the variation pattern of nickel futures prices using the daily closing levels of the nickel futures price index of the London Futures Exchange and the Shanghai Futures Exchange. The data coarse-graining method is employed to transform the continuous time series data of price index changes into symbols {P, N, M}, which are slid through continuous windows to form the modalities of price index linkage fluctuations. By treating the modalities as nodes and the transformations between them as edges, a weighted directed complex network is constructed to represent the linked volatility of the LME and SHFE nickel futures indices time series. The complex network is applied to analyse the network characteristics and obtain the inner pattern of the linked fluctuations. The results show that the complex network of time series linked volatility of the LME and SHFE nickel futures indices exhibits a power-law nature, with closely linked subgroups formed within it. And the mode transitions within these subgroups follow certain patterns. This paper also identifies core positioned modes and important intermediate modes that reflect the dynamics of nickel prices in reality. The method presented in this paper may be extended to related fields and has good applicability.

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Long Range Forecasting (From Crystal Ball to Computer).

Abstract: Long Range Forecasting (from Crystal Ball to Computer), by J. Scott Armstrong, Wiley, 612 pp.

Cited by 72 publications

References 0 publications

Quality Analysis of Photovoltaic System Using Descriptive Statistics of Power Performance Index

Quality Analysis of Photovoltaic System Using Descriptive Statistics of Power Performance Index

Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Analysis of Linkage Fluctuation in Time Series Data of Nickel Futures Price Index

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