Error modeling of demand patterns to improve forecasting accuracy
This study aims to estimate and model error patterns to reduce forecast error and improve forecast accuracy for time series data. The objective is to assess the impact of incorporating error patterns as features in long short-term memory and transformer neural network models. The research employs a...
Main Author: | Sa, Ziheng |
---|---|
Other Authors: | Jagath C Rajapakse |
Format: | Final Year Project (FYP) |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/175223 |
Similar Items
-
Simple noise-reduction method based on nonlinear forecasting
by: Tan, James Peng Lung
Published: (2018) -
Time series demand forecasting on NYC taxi dataset
by: Teh, Timothy Rui Sheng
Published: (2024) -
Computational Intelligence approaches in forecasting demand of essential medical products
by: Chung, Suhwan
Published: (2025) -
DCEnt‐PredictiveNet: a novel explainable hybrid model for time series forecasting
by: Sudarshan, Vidya K., et al.
Published: (2024) -
Bayesian optimization based dynamic ensemble for time series forecasting
by: Du, Liang, et al.
Published: (2022)