Dynamic Lagging for Time-Series Forecasting in E-Commerce Finance: Mitigating Information Loss with A Hybrid ML Architecture

• URL: http://arxiv.org/abs/2509.20244v1
• Date: Wed, 24 Sep 2025 15:33:16 GMT
• Title: Dynamic Lagging for Time-Series Forecasting in E-Commerce Finance: Mitigating Information Loss with A Hybrid ML Architecture
• Authors: Abhishek Sharma, Anat Parush, Sumit Wadhwa, Amihai Savir, Anne Guinard, Prateek Srivastava,
• Abstract summary: We propose a hybrid forecasting framework that integrates dynamic lagged feature engineering and adaptive rolling-window representations.<n>Our approach explicitly incorporates invoice-level behavioral modeling, structured lag of support data, and custom stability-aware loss functions.
• Score: 0.8192992814374568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate forecasting in the e-commerce finance domain is particularly challenging due to irregular invoice schedules, payment deferrals, and user-specific behavioral variability. These factors, combined with sparse datasets and short historical windows, limit the effectiveness of conventional time-series methods. While deep learning and Transformer-based models have shown promise in other domains, their performance deteriorates under partial observability and limited historical data. To address these challenges, we propose a hybrid forecasting framework that integrates dynamic lagged feature engineering and adaptive rolling-window representations with classical statistical models and ensemble learners. Our approach explicitly incorporates invoice-level behavioral modeling, structured lag of support data, and custom stability-aware loss functions, enabling robust forecasts in sparse and irregular financial settings. Empirical results demonstrate an approximate 5% reduction in MAPE compared to baseline models, translating into substantial financial savings. Furthermore, the framework enhances forecast stability over quarterly horizons and strengthens feature target correlation by capturing both short- and long-term patterns, leveraging user profile attributes, and simulating upcoming invoice behaviors. These findings underscore the value of combining structured lagging, invoice-level closure modeling, and behavioral insights to advance predictive accuracy in sparse financial time-series forecasting.

Related papers

• Financial time series augmentation using transformer based GAN architecture [0.0]
  We show how Generative Adrial Networks (GANs) can effectively serve as a data augmentation tool to overcome data scarcity in the financial domain.<n>Specifically, we show that training a Long Short-Term Memory (LSTM) forecasting model on a dataset augmented with synthetic data generated by a transformer-based GAN (TTS-GAN) significantly improves the forecasting accuracy compared to using real data alone.
  arXiv  Detail & Related papers  (2026-02-19T22:02:09Z)
• StockBot 2.0: Vanilla LSTMs Outperform Transformer-based Forecasting for Stock Prices [0.0]
  We present an enhanced StockBot architecture that systematically evaluates modern attention-based, convolutional, and recurrent time-series forecasting models.<n>A carefully constructed vanilla LSTM consistently achieves superior predictive accuracy and more stable buy/sell decision-making.
  arXiv  Detail & Related papers  (2026-01-01T04:09:51Z)
• Synthetic Financial Data Generation for Enhanced Financial Modelling [0.0]
  This paper presents a unified multi-criteria evaluation framework for synthetic financial data.<n>Using historical S and P 500 daily data, we evaluate fidelity (Maximum Mean Discrepancy, MMD), temporal structure (autocorrelation and volatility clustering), and practical utility in downstream tasks.<n>We articulate practical guidelines for selecting generative models according to application needs and computational constraints.
  arXiv  Detail & Related papers  (2025-12-25T21:43:16Z)
• Estimating Time Series Foundation Model Transferability via In-Context Learning [74.65355820906355]
  Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training.<n>Fine-tuning remains critical for boosting performance in domains with limited public data.<n>We introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem.
  arXiv  Detail & Related papers  (2025-09-28T07:07:13Z)
• Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensembles [7.787518725874443]
  Time series foundation models (TSFMs) have shown strong generalization and zero-shot capabilities for time series forecasting, anomaly detection, classification, and imputation.<n>This paper investigates a suite of statistical and ensemble-based enhancement techniques to improve robustness and accuracy.
  arXiv  Detail & Related papers  (2025-08-18T04:06:26Z)
• Tensor-Empowered Asset Pricing with Missing Data [2.3404331106562677]
  We introduce an Adaptive, Cluster-based Temporal smoothing tensor completion framework (ACT-Tensor) for missing financial data panels.<n>ACT-Tensor consistently outperforms state-of-the-art benchmarks in terms of imputation accuracy across a range of missing data regimes.<n>Results show that ACT-Tensor not only achieves accurate return forecasting but also significantly improves risk-adjusted returns of the constructed portfolio.
  arXiv  Detail & Related papers  (2025-08-03T17:28:57Z)
• Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism [5.480591342227219]
  This paper proposes a new framework, AEFIN, which enhances the information sharing ability between stable and unstable components.<n>We also design a new loss function that combines time-domain stability constraints, time-domain instability constraints, and frequency-domain stability constraints to improve the accuracy and robustness of forecasting.<n> Experimental results show that AEFIN outperforms the most common models in terms of mean square error and mean absolute error, especially under non-stationary data conditions.
  arXiv  Detail & Related papers  (2025-05-11T09:34:36Z)
• LLM4FTS: Enhancing Large Language Models for Financial Time Series Prediction [0.0]
  Traditional machine learning models exhibit limitations in this forecasting task constrained by their restricted model capacity.<n>We propose $LLM4FTS$, a novel framework that enhances temporal sequence modeling through learnable patch segmentation and dynamic wavelet convolution modules.<n>Experiments on real-world financial datasets substantiate the framework's efficacy, demonstrating superior performance in capturing complex market patterns and achieving state-of-the-art results in stock return prediction.
  arXiv  Detail & Related papers  (2025-05-05T06:48:34Z)
• BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges [55.2480439325792]
  This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies.
  arXiv  Detail & Related papers  (2024-11-09T05:40:32Z)
• Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting [65.40983982856056]
  We introduce STOIC, that leverages correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides 16% more accurate and better-calibrated forecasts.
  arXiv  Detail & Related papers  (2024-07-02T20:14:32Z)
• Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens [10.103561529332184]
  We focus on multi-modal time-series forecasting, where imprecision due to noisy or incorrect data can lead to erroneous predictions. We introduce a rating methodology to assess the robustness of Multi-Modal Time-Series Forecasting Models.
  arXiv  Detail & Related papers  (2024-06-12T17:39:16Z)
• Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting [46.63798583414426]
  Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks.
  arXiv  Detail & Related papers  (2024-01-22T13:15:40Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
  Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
  arXiv  Detail & Related papers  (2021-07-05T10:15:23Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.