scholarly journals Time series classification based on arima and adaboost

2020 ◽  
Vol 309 ◽  
pp. 03024
Author(s):  
Jinghui Wang ◽  
Shugang Tang
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Time Series Classification ◽  
Average Model ◽  
Adaptive Boosting ◽  
Moving Average Model ◽  
Auto Regressive ◽  
Simulation Results

In this paper, a novel time series classification approach, which using auto regressive integrated moving average model (ARIMA) features and Adaptive Boosting (AdaBoost) classifications. ARIMA is particularly suitable for distinguishing time series signal and Adaboost is suitable for features classification. The simulation results have shown that the algorithm is feasible. And this method is more accurate than many existing method in multiple time series problems.

scholarly journals Food Predictive Anatomization using Time Series

2020 ◽  
Vol 9 (9) ◽  
pp. 14-17
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Moving Average ◽  
Series Data ◽  
Market Exchange ◽  
Average Model ◽  
Moving Average Model ◽  
Auto Regressive

Time series survey and forecasting upcoming values has been a research focus past years ago. Time series analysis and predict The time-series data finds its importance in various roles of implementation such as business, stock market exchange, weather forecasting, electricity demand, cost and usage of products such as fuels, etc. In this project, a detailed survey of the various techniques applied for forecasting different method of time series datasets are provided. Moving average model and Auto-Regressive Integrated Moving Average model with a case study on food predictive analysis time series data with R software.

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