scholarly journals EVALUATION OF COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR DAILY PRODUCT SALES FORECASTING

2015 ◽  
pp. 157-164
Author(s):  
Gediminas Gediminas Žylius ◽  
Rimvydas Simutis ◽  
Vygandas Vaitkus
Keyword(s):  
Time Series ◽  
Computational Intelligence ◽  
Real Life ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Historical Period ◽  
Support Vector Regression Model ◽  
Forecasting Performance ◽  
Intelligence Models ◽  
Product Sales

Product sales forecasting is crucial task in inventory control and whole supply chain management. Accuracy of sales forecasting determines product logistics performance. In this paper we present study that aims to answer three questions: what input set is most informative for daily sales time series forecasting; do weather input features improve forecasting performance; what computational intelligence model is most appropriate for daily sales forecasting. In order to answer those questions we selected three computational intelligence models that are used for regression task together with various input sets for daily time series forecasting. Data collected consist of 89 real life product sales time series from various stores with historical period of 15 months. Results show that most useful input set is extracted from time series itself. Secondly, research results show that weather features do not improve forecasting performance. And finally, best forecasting results are achieved using support vector regression model.

Time series forecasting by a seasonal support vector regression model

2010 ◽  
Vol 37 (6) ◽  
pp. 4261-4265 ◽  
Author(s):  
Ping-Feng Pai ◽  
Kuo-Ping Lin ◽  
Chi-Shen Lin ◽  
Ping-Teng Chang
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Support Vector Regression Model

Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

2018 ◽  
Vol 233 (7) ◽  
pp. 701-714
Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
High Speed ◽  
Support Vector ◽  
High Speed Train ◽  
Support Vector Regression Model ◽  
Vibration Time ◽  
Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

scholarly journals Explaining Bad Forecasts in Global Time Series Models

2021 ◽  
Vol 11 (19) ◽  
pp. 9243
Author(s):  
Jože Rožanec ◽  
Elena Trajkova ◽  
Klemen Kenda ◽  
Blaž Fortuna ◽  
Dunja Mladenić
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Time Series Forecasting ◽  
Modular Architecture ◽  
Detection Algorithms ◽  
Training Samples ◽  
Forecasting Performance ◽  
Global Time ◽  
Real World Datasets ◽  
Value Changes

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and explainable artificial intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand how value changes in the feature vector can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.

scholarly journals Time-Series Forecasting Models for Gasoline Prices in China

2018 ◽  
Vol 10 (12) ◽  
pp. 43
Author(s):  
Feng Xu ◽  
Mohamad Sepehri ◽  
Jian Hua ◽  
Sergey Ivanov ◽  
Julius N. Anyu
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Gasoline Prices ◽  
Support Vector ◽  
Grey System ◽  
Gasoline Price ◽  
Forecasting Models ◽  
Long Run ◽  
Environmental Protection Policies

Accurate prediction of gasoline price is important for the automobile makers to adjust designs and productions as well as marketing plans of their products. It is also necessary for government agencies to set effective inflation monitoring and environmental protection policies. To predict future levels of the gasoline price, due to difficulties of obtaining accurate estimates of influential external factors, data driven time-series forecasting models thus become more suitable given the convenience and practicability they are providing. In this paper, five popular time-series forecasting models, i.e., ARIMA-GARCH, exponential smoothing, grey system, neural network, and support vector machines models, are applied to predict gasoline prices in China. Comparing the performances of these models, it is noted that for this specific time series, a parsimonious ARIMA model performs the best in predicting the gasoline prices for a short time horizon, while for the medium length and long run the SVR and FNN models outperforms others respectively.  

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