Zhang et al 2026c - Revision history

Scipediacontent: Scipediacontent moved page Review 500874945847 to Zhang et al 2026c

2026-01-28T10:41:21Z

Scipediacontent moved page Review 500874945847 to Zhang et al 2026c

Scipediacontent at 10:41, 28 January 2026

2026-01-28T10:41:05Z

Scipediacontent at 10:40, 28 January 2026

2026-01-28T10:40:41Z

JSanchez: JSanchez moved page Draft Sanchez Pinedo 389491163 to Review 500874945847

2026-01-27T09:15:26Z

JSanchez moved page Draft Sanchez Pinedo 389491163 to Review 500874945847

JSanchez: Created page with " == Abstract ==
This study introduces a Lasso–Prophet hybrid framework developed to deal with the limitations and gap of Facebook’s Prophet model. The approach..."

2026-01-27T09:15:24Z

Created page with " == Abstract == <p>This study introduces a Lasso–Prophet hybrid framework developed to deal with the limitations and gap of Facebook’s Prophet model. The approach..."

New page

== Abstract ==

<p>This study introduces a Lasso–Prophet hybrid framework developed to deal with the limitations and gap of Facebook’s Prophet model. The approach begins with Prophet’s decomposition of a time series into its fundamental components trend, seasonality, and holiday effects, and then applies Lasso regression to the residuals to capture additional structural patterns that fail to capture by the base model. This layered methodology boosts predictive accuracy by enabling the model to learn both systematic and irregular variations within temporal data by incorporating Lasso’s feature selection capability, the framework efficiently handles highdimensional datasets, retaining only the most informative predictors. The outcome is a hybrid model which achieves an optimal balance among interpretability, scalability, sparsity, and forecasting precision. Validation on simulated high-feature datasets and real-world electricity consumption data demonstrates that the Lasso–Prophet hybrid consistently outperforms the Prophet and other baseline models.OPEN ACCESS Received: 31/08/2025 Accepted: 17/11/2025 Published: 23/01/2026</p>

== Document ==
<pdf>Media:Draft_Sanchez Pinedo_389491163-8281-document.pdf</pdf>

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	== Document ==		== Document ==
−	<pdf>Media:~~Draft_Sanchez Pinedo_389491163-8281-document~~.pdf</pdf>	+	<pdf>Media:Review_500874945847_3255_39. TSP_RIMNI_71075.pdf</pdf>

← Older revision	Revision as of 10:41, 28 January 2026
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 == Abstract ==
-<p>This study introduces a Lasso&ndash;Prophet hybrid framework developed to deal with the limitations and gap of Facebook&rsquo;s Prophet model. The approach begins with Prophet&rsquo;s decomposition of a time series into its fundamental components trend, seasonality, and holiday effects, and then applies Lasso regression to the residuals to capture additional structural patterns that fail to capture by the base model. This layered methodology boosts predictive accuracy by enabling the model to learn both systematic and irregular variations within temporal data by incorporating Lasso&rsquo;s feature selection capability, the framework efficiently handles highdimensional datasets, retaining only the most informative predictors. The outcome is a hybrid model which achieves an optimal balance among interpretability, scalability, sparsity, and forecasting precision. Validation on simulated high-feature datasets and real-world electricity consumption data demonstrates that the Lasso&ndash;Prophet hybrid consistently outperforms the Prophet and other baseline models.OPEN ACCESS Received: 31/08/2025 Accepted: 17/11/2025 Published: 23/01/2026</p>
+<p>The application of compensated foundations is common in engineering, but significant settlements often occur during the application process. A foundation pit model supported by a rigid retaining wall was established based on the Finite Element Method-Discrete Element Method (FEMDEM), and a cantilevered layer-by-layer excavation process was simulated. A rolling resistance linear model was used to simulate the sand, while a variable particle size method was adopted to establish the foundation model. It is shown that during the excavation process of the foundation pit, the stress of the sand at the pit bottom gradually decreases, and the displacement of the soil changes gradually fromsettlement to uplift as one moves frombehind the retaining wall to the bottomof the foundation pit. Moreover, theporosityof the sandat thepit bottomgradually increases.As a result, the strength of the uplifted sand at the pit bottomdecreases due to over-excavation during the foundation pit excavation stage. The uplifted sand is finally excavated during the site leveling stage, resulting in a further decrease in the strength of the sand at the pit bottom. Finally, an inverted arch bottom plate structure is proposed to mitigate the over-excavation settlement of the compensated foundation.</p>
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 == Document ==
 <pdf>Media:Draft_Sanchez Pinedo_389491163-8281-document.pdf</pdf>