Adaptive Holt-Winters Forecasting Method based on Artificial Intelligence Techniques

Sangkhiew, Noppakun; Watanasungsuit, Arnat; Inthawongse, Choat; Jomthong, Peerapop; Saicharoen, Kawinthorn; Pornsing, Choosak

doi:10.1109/imcom56909.2023.10035579

Cited by 2 publications

(1 citation statement)

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“…Liu predicted the short-term spot price trend of steel plates using an autoregressive moving average (ARIMA) model, a long-and short-term memory (LSTM) model, and a combined LSTM-ARIMA model [20]. Sangkhiew made two improvements to the Holt-Winter (HW) model, combined with artificial intelligence techniques to establish PSO-HW and GSA-HW models to measure local stainless-steel prices [21].…”

Section: Introductionmentioning

confidence: 99%

Cost Forecasting for Building Rebar under Uncertainty Conditions: Methodology and Practice

Dai,

Gao,

2024

Buildings

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As large-scale infrastructure construction projects conclude, the overall civil construction market shrinks, leading to increased competition among construction companies. Accordingly, various construction companies are gradually emphasizing the issue of project costs. Numerous studies have shown the impact of material costs on the overall project cost. However, sharp fluctuations in material prices have been observed in recent years due to various unstable factors in the market. Thus, accurate prediction of material prices facilitates the development of appropriate material procurement strategies to deal with market risks. This paper collects the rebar prices announced in the first half of 2023 in China’s Guangdong Province, selects one type of rebar price as a representative, and analyzes the time series characteristics of the rebar price composition. Then, it judges whether the time series passes the white noise detection, grey correlation detection, and level ratio detection. The prediction model is established based on the seasonal auto-regressive integrated moving average (SARIMA) model and the grey model (GM) (1.1) to predict future rebar prices. According to the characteristics of the rebar price data in June 2023, the residual inverse method combines the results predicted by the two models. The price in the early and middle of July 2023 is then predicted using the newly constructed combined model. The results indicate that the combined model is more accurate than the single prediction model.

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Section: Introductionmentioning

confidence: 99%