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on Forecasting |
| By: | Daniele Girolimetto; George Athanasopoulos; Tommaso Di Fonzo; Rob J Hyndman |
| Abstract: | Forecast reconciliation is a post-forecasting process that involves transforming a set of incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for a multivariate time series. In this paper we extend the current state-of-the-art cross-sectional probabilistic forecast reconciliation approach to encompass a cross-temporal framework, where temporal constraints are also applied. Our proposed methodology employs both parametric Gaussian and non-parametric bootstrap approaches to draw samples from an incoherent crosstemporal distribution. To improve the estimation of the forecast error covariance matrix, we propose using multi-step residuals, especially in the time dimension where the usual one-step residuals fail. To address high-dimensionality issues, we present four alternatives for the covariance matrix, where we exploit the twofold nature (cross-sectional and temporal) of the cross-temporal structure, and introduce the idea of overlapping residuals. We evaluate the proposed methods through a detailed simulation study that investigates their theoretical and empirical properties. We further assess the effectiveness of the proposed cross-temporal reconciliation approach by applying it to two empirical forecasting experiments, using the Australian GDP and the Australian Tourism Demand datasets. For both applications, we show that the optimal cross-temporal reconciliation approaches significantly outperform the incoherent base forecasts in terms of the Continuous Ranked Probability Score and the Energy Score. Overall, our study expands and unifies the notation for cross-sectional, temporal and cross-temporal reconciliation, thus extending and deepening the probabilistic cross-temporal framework. The results highlight the potential of the proposed cross-temporal forecast reconciliation methods in improving the accuracy of probabilistic forecasting models. |
| Keywords: | coherent, GDP, linear constraints, multivariate time series, temporal aggregation, tourism flows |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2023-6&r=for |
| By: | Mayank Ratan Bhardwaj (Indian Institute of Science); Jaydeep Pawar (Indian Institute of Science); Abhijnya Bhat (PES University); Deepanshu (Indian Institute of Science); Inavamsi Enaganti (Indian Institute of Science); Kartik Sagar (Indian Institute of Science); Y. Narahari (Indian Institute of Science) |
| Abstract: | Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices. This is a technically challenging problem, which has been attempted before. In this paper, we propose an innovative deep learning based approach to achieve increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. Our approach works well with noisy legacy data and produces a performance that is at least 20% better than the results available in the literature. We are able to predict prices up to 30 days ahead. We choose two vegetables, potato (stable price behavior) and tomato (volatile price behavior) and work with noisy public data available from Indian agricultural markets. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.09761&r=for |
| By: | Li Tang; Chuanli Tang; Qi Fu |
| Abstract: | Accurate and reliable prediction of individual travel mode choices is crucial for developing multi-mode urban transportation systems, conducting transportation planning and formulating traffic demand management strategies. Traditional discrete choice models have dominated the modelling methods for decades yet suffer from strict model assumptions and low prediction accuracy. In recent years, machine learning (ML) models, such as neural networks and boosting models, are widely used by researchers for travel mode choice prediction and have yielded promising results. However, despite the superior prediction performance, a large body of ML methods, especially the branch of neural network models, is also limited by overfitting and tedious model structure determination process. To bridge this gap, this study proposes an enhanced multilayer perceptron (MLP; a neural network) with two hidden layers for travel mode choice prediction; this MLP is enhanced by XGBoost (a boosting method) for feature selection and a grid search method for optimal hidden neurone determination of each hidden layer. The proposed method was trained and tested on a real resident travel diary dataset collected in Chengdu, China. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.12698&r=for |