Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark

Key points

• Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk.1
• Unlike structured financial markets, second-hand electronics exhibit high volatility, sparse listing histories, and non-normal price dynamics - yet no systematic time-series benchmark exists for this domain.1
• We use a large-scale dataset of daily price listings from Polish online marketplaces (January 2022 to March 2025, 100+ smartphone and laptop models) and evaluate eleven models across six horizons from 1 to 365 days, covering classical methods (ARIMA, ETS, Theta), recurrent and convolutional networks (LSTM, TCN), and modern deep architectures (N-BEATS, N-HiTS, TFT, PatchTST, Informer).1
• Three complementary evaluation protocols assess trajectory fitness, one-shot endpoint accuracy, and cross-horizon transfer.1

Sources (1)

• [1]Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 06:05 PM

  This paper presents the first multi-horizon benchmark of statistical and deep learning forecasting models for used electronics price prediction.

  Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk.

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