---
title: "Fuzzy selective state model for forecasting the dynamics of consumer preferences"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.41879v1"
permalink: "https://feed.astralor.com/items/itm_ae87d7f2008d1381951fa9fc"
published: "2026-10-30T15:27:14.000Z"
score: 56
channel: "finance"
tags: ["Fuzzy","Mamba","Mamba-type","Matveev","Zadeh"]
---

# Fuzzy selective state model for forecasting the dynamics of consumer preferences

## At a glance

We consider forecasting the dynamics of consumer preferences of retail chain customers. A customer's preference is described by a triangular fuzzy number. The evolution of the preference over time is governed by a Mamba…

## Key points

- We consider forecasting the dynamics of consumer preferences of retail chain customers.
- A customer's preference is described by a triangular fuzzy number.
- The evolution of the preference over time is governed by a Mamba-type selective state space model whose hidden state is a triangular number, with computations carried out in M.G.~Matveev's two-component algebra of fuzzy…

## Full text

We consider forecasting the dynamics of consumer preferences of retail chain customers. A customer's preference is described by a triangular fuzzy number. The evolution of the preference over time is governed by a Mamba-type selective state space model whose hidden state is a triangular number, with computations carried out in M.G.~Matveev's two-component algebra of fuzzy numbers. The recurrence parameters are computed from crisp features, while the fuzzy part of the model remains linear with non-negative coefficients. We show that the state then always remains a proper triangular number and is a convex combination of past observations, so that the fuzziness of the forecast does not accumulate over time and the algebraic computations coincide with Zadeh's extension principle. On a synthetic example with competitor promotions and customer switching, a lightweight 107-parameter model reaches a mean absolute error of the forecast centre of 0.154 versus 0.163 for the constant-inertia evolution operator and 0.179 for the naive forecast, matches a crisp model of the same architecture and additionally provides a linguistically interpretable estimate of forecast vagueness. We also show how the linguistic readout of the forecast can be used to detect a permanent switch of a customer to a competitor.

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