A fuel sales forecast method based on variational Bayesian structural time series

Abstract

Fuel prices, which are of broad concern to the general public, are always seen as a challenging research topic. This paper proposes a variational Bayesian structural time-series model (STM) to effectively process complex fuel sales data online and provide real-time forecasting of fuel sales. While a traditional STM normally uses a probability model and the Markov chain Monte Carlo (MCMC) method to process change points, using the MCMC method to train the online model can be difficult given a relatively heavy computing load and time consumption. We thus consider the variational Bayesian STM, which uses variational Bayesian inference to make a reliable judgment of the trend change points without relying on artificial prior information, for our prediction method. With the inferences being driven by the data, our model passes the quantitative uncertainties to the forecast stage of the time series, which improves the robustness and reliability of the model. After conducting several experiments by using a self-collected dataset, we show that compared with a traditional STM, the proposed model has significantly shorter computing times for approximate forecast precision. Moreover, our model improves the forecast efficiency for fuel sales and the synergy of the distributed forecast platform based on an architecture of network.

Design of Drug Sales Forecasting Model Using Particle Swarm Optimization Neural Networks Model

“…The improved NN by Yucesan et al made self-calibration with reference to the prediction of synchronous time series and used a genetic algorithm to achieve self-optimization through calibration, simplifying the network structure [ 11 ]. Lian et al took the preliminary prediction results as the input of the improved BPNN, then trained and predicted on this basis, and constructed a combined prediction model based on the improved BPNN [ 12 ]. Aiming at the characteristics of a company's drug sales, inventory, and the main problems in the sales process, Duan et al proposed a hierarchical clustering model for multivariety drug sales forecast to predict the company's drug sales [ 13 ].…”