Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble

• URL: http://arxiv.org/abs/2605.30720v1
• Date: Fri, 29 May 2026 01:25:32 GMT
• Title: Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble
• Abstract summary: Kalimati Vegetable Price Index aggregates 135 daily wholesale commodities from Kathmandu over ten years (2013-2023)<n>A rich set of 64 causally valid features was developed, including festival lead-lag effects, rolling statistics, and calendar variables.<n>14 forecasting models spanning statistical, tree-based, deep learning, hybrid, and transformer architectures were rigorously evaluated.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural influences on demand. This study introduces the Kalimati Vegetable Price Index (KVPI), a new inverse-volatility weighted composite index that aggregates 135 daily wholesale commodities from Kathmandu over ten years (2013-2023). By creating a stable macro-level signal, the KVPI reduces the noise inherent in modelling individual crops. A rich set of 64 causally valid features was developed, including festival lead-lag effects, rolling statistics, and calendar variables. Fourteen forecasting models spanning statistical, tree-based, deep learning, hybrid, and transformer architectures were rigorously evaluated across short (7-day), medium (14- and 30-day), and long-term (90-day) horizons. Tree-based ensembles proved notably robust, while classical statistical models and complex transformers struggled with the noisy dataset. The proposed Momentum-Corrected Online Stacking Ensemble achieved the strongest performance, yielding a Root Mean Square Error (RMSE) of 1.771, an exceptionally low Mean Absolute Percentage Error (MAPE) of 0.68%, and explaining 84.5% of the variance (R-squared = 0.845) at the 90-day horizon. This open-source pipeline provides policymakers and supply chain actors in Nepal and similar markets with a practical, reliable tool for anticipating price movements and strengthening food security.

Related papers

• Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generation [0.0]
  Five model classes are trained on daily observations from approximately 300 large-cap US equities over eleven years.<n>No individual deep learning architecture outperforms gradient-boosted trees, but combining XGBoost and TabNet using rank aggregation produces a Hybrid ensemble with an annualised return of 51.26%.<n>A near-zero beta indicates this outperformance is driven by stock selection, not market exposure.
  arXiv  Detail & Related papers  (2026-08-27T13:01:32Z)
• DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting [43.38565681834944]
  We propose DecoVAE, a lightweight interpretable trend-seasonal VAE framework.<n>It decomposes time series into trend and seasonal components by applying domain-specific inductive biases.<n>It consistently outperforms strong baselines in seven real-world benchmarks.
  arXiv  Detail & Related papers  (2026-08-20T13:51:07Z)
• Federated Ensemble Forecasting Under Supply-Chain Market Volatility [0.0]
  Supply chain forecasting systems increasingly operate under market shocks, non-identically distributed regional demand, and limited willingness to centralize commercial data.<n>This work proposes Federated Ensemble Forecasting with Negative-Correlation Learning (FEF NCL), a distributed method that trains specialized forecasting experts across client nodes while discouraging redundant model errors.<n>The framework combines temporal feature encoders, client level drift scoring, reliability-weighted aggregation, and an explain ability layer that exposes the market and supplier variables most responsible for each forecast.
  arXiv  Detail & Related papers  (2026-08-05T20:15:41Z)
• When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets [1.5164041225909122]
  Vegetable prices in Sri Lanka are highly volatile because the market is largely import-isolated.<n>This study develops a machine learning framework to forecast such volatility by incorporating supply-chain-aware features.
  arXiv  Detail & Related papers  (2026-06-28T07:30:47Z)
• Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models [37.24612885977265]
  Vision-language models (VLMs) such as CLIP show strong zero-shot generalization but remain highly vulnerable to adversarial attacks.<n>Recent approaches exploit how CLIP's visual representations respond to perturbations.<n>We propose a training-free, plug-in drift-gated mechanism that uses high-noise feature drift as a lightweight gating signal.
  arXiv  Detail & Related papers  (2026-06-02T14:49:04Z)
• Differentiable latent structure discovery for interpretable forecasting in clinical time series [38.473526928012724]
  We introduce StructGP, a continuous-time multi-task Gaussian process that couples differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.<n>We propose LP-StructGP, which augments StructGP with latent pathways-shared, temporally shifted trajectories inferred via subject-specific coupling filters and a softmax gating mechanism-to capture cross-patient progression patterns.
  arXiv  Detail & Related papers  (2026-04-30T14:59:50Z)
• A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset [1.9635288496224899]
  AgriBD is a benchmark dataset of 1,779 daily retail mid-prices for five Bangladeshi commodities.<n>We evaluate seven forecasting approaches spanning classical models and deep learning architectures.<n>All code, models, and data are released publicly to support replication and future forecasting research.
  arXiv  Detail & Related papers  (2026-03-27T20:21:52Z)
• Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Approach with Jump-Diffusion [0.0]
  Existing approaches, from parametric models to deep generative networks, struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation, and persistent volatility clustering.<n>We propose a hybrid hidden Markov framework that discretizes continuous excess growth rates into Laplace quantile-defined market states.<n> Parameters are estimated by direct transition counting, bypassing the Baum-Welch EM algorithm.
  arXiv  Detail & Related papers  (2026-03-10T20:06:53Z)
• Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis [1.2362187555287152]
  This paper presents an integrated framework combining a node transformer architecture with BERT-based sentiment analysis for stock price forecasting.<n>The proposed model represents the stock market as a graph structure where individual stocks form nodes and edges capture relationships.<n> Experiments on 20 S&P 500 stocks spanning January 1982 to March 2025 demonstrate that the integrated model achieves a mean absolute percentage error (MAPE) of 0.80% for one-day-ahead predictions.
  arXiv  Detail & Related papers  (2026-03-06T05:15:22Z)
• ReLE: A Scalable System and Structured Benchmark for Diagnosing Capability Anisotropy in Chinese LLMs [37.23311145049677]
  We present ReLE, a scalable system designed to diagnose Capability Anisotropy.<n>We evaluate 304 models across a Domain $times$ Capability Symbolic matrix comprising 207,843 samples.
  arXiv  Detail & Related papers  (2026-01-24T09:57:59Z)
• Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning [41.52380204321823]
  This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning.<n>We evaluate UAMDP in two domains-high-frequency equity trading and retail inventory control-both marked by structural uncertainty and economic volatility.
  arXiv  Detail & Related papers  (2025-10-09T13:46:32Z)
• Forking-Sequences [33.62694927994466]
  Even highly accurate models can produce erratic revisions between forecast creation dates.<n>Models like MQCNN, MQT, and SPADE employ a little-known but highly effective technique: forking-sequences.<n>We demonstrate three key benefits of forking-sequences: (i) more stable and consistent gradient updates during training; (ii) reduced forecast variance through ensembling; and (iii) improved inference efficiency.
  arXiv  Detail & Related papers  (2025-10-06T04:51:06Z)
• Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News [2.5165775267615205]
  This paper introduces a hybrid forecasting framework that combines historical commodity price data with semantic signals derived from global economic news.<n>The architecture integrates dual-stream Long Short-Term Memory (LSTM) networks with attention mechanisms to fuse structured time-series inputs with semantically embedded, fact-checked news summaries.<n>Results show that the proposed approach achieves a mean AUC of 0.94 and an overall accuracy of 0.91 substantially outperforming traditional baselines.
  arXiv  Detail & Related papers  (2025-07-24T20:52:47Z)
• A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images [8.073306549051802]
  This paper proposes a novel method, named Sequence-based Multi-scale Fusion Regression Convolutional Neural Network (SMSFR-CNN) for predicting stock price movements in the China A-share market. By utilizing CNN to learn sequential features and combining them with image features, we improve the accuracy of stock trend prediction on the A-share market stock dataset.
  arXiv  Detail & Related papers  (2024-10-25T03:50:54Z)
• Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows [62.997667081978825]
  In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts.
  arXiv  Detail & Related papers  (2022-05-27T08:38:20Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)

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.