Detecting changes in signals and systems—a survey
Automatica (Journal of IFAC)
Multilayer feedforward networks are universal approximators
Neural Networks
Interaction of judgemental and statistical forecasting methods: issues &
Management Science
Neural network models for time series forecasts
Management Science
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
Computational Statistics & Data Analysis
Knowledge-Based Event Detection in Complex Time Series Data
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
A Gibbs sampling scheme to the product partition model: an application to change-point problems
Computers and Operations Research
A Dynamic Changepoint Model for New Product Sales Forecasting
Marketing Science
Robust forecasting of mortality and fertility rates: A functional data approach
Computational Statistics & Data Analysis
A prediction algorithm for time series based on adaptive model selection
Expert Systems with Applications: An International Journal
Neural network based temporal feature models for short-term railway passenger demand forecasting
Expert Systems with Applications: An International Journal
Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
| Hi-index | 12.05 |
The hotel and car manufacturing industries share many common points in their sales forecasting. For example, both are greatly affected by the fluctuation of economy, and closely related to the inertia. According to the principle characters of forecasting problem concerning these two kinds of industries, a short-term quantitative sales forecasting model is proposed based on the economic fluctuation analysis and the nai@?ve forecasting technology. The sales time series and its curve are used to construct this model. The relative concepts of the model are presented and corresponding algorithms are brought forward. Firstly, economic fluctuation of products sales is analyzed and the historical patterns of economic fluctuation change are divided. According to the geometric characteristics of a sales curve, the best historical matching for the current status is then found out, which corresponds to the process of activating the historical experiences of a manager. Finally the changing trend of the sales curve in the next period is determined, from which the short-term sales forecasting results can be obtained. The number of scattered guests of a hotel and the short-term sales for cars manufactured by a factory are forecasted by means of the model, which shows satisfactory forecasting accuracy. In fact, the forecasting approach proposed herein is the mathematical representation of the naive forecasting method that is a kind of regular deduction based on the similarity between historical pattern and current status. Thus, this approach is good at forecasting the time series with the similarity between historical pattern and current status no matter whether the time series is seasonal or not, and gives better forecasting accuracy than ARMA and ANN models.