Robustness properties of some forecasting methods for seasonal time series: A Monte Carlo study

1997 ◽  
Vol 13 (2) ◽  
pp. 269-280 ◽  
Author(s):  
Chunhang Chen
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Study ◽  
Seasonal Time Series ◽  
Forecasting Methods

scholarly journals Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis

2019 ◽  
Vol 10 (4) ◽  
pp. 1324
Author(s):  
Kevin William Matos Paixão ◽  
Adriano Maniçoba da Silva
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Moving Average ◽  
Prediction Methods ◽  
Future Scenario ◽  
Sales Forecast ◽  
Forecasting Methods ◽  
Weighted Moving Average

Organizations today are required to be prepared for future situations. This preparation can generate a significant competitive advantage. In order to maximize benefits, several companies are investing more in techniques that simulate a future scenario and enable more precise and assertive decision making. Among these techniques are the sales forecasting methods. The comparison between the known techniques is an important factor to increase the assertiveness of the forecast. The objective of this study was to compare the sales forecast results of a mechanical components manufacturing company obtained through five different techniques, divided into two groups, the first one, which uses the fundamentals of the time series, and the second one is the Monte Carlo simulation. The following prediction methods were compared: moving average, weighted moving average, least squares, holt winter and Monte Carlo simulation. The results indicated that the methods that obtained the best performance were the moving average and the weighted moving average attaining 94% accuracy.

scholarly journals INFERENCE ON NONSTATIONARY TIME SERIES WITH MOVING MEAN

2014 ◽  
Vol 32 (2) ◽  
pp. 431-457 ◽  
Author(s):  
Jiti Gao ◽  
Peter M. Robinson
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Study ◽  
Semiparametric Model ◽  
Nonstationary Time Series ◽  
Finite Sample ◽  
Trend Function ◽  
Deterministic Trend ◽  
Short Memory ◽  
Memory Parameter

A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I(1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.

scholarly journals Strengths and weaknesses of S-curves

2021 ◽  
Author(s):  
THEODORE MODIS
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Natural Law ◽  
Average Rate ◽  
Monte Carlo Study ◽  
Logistic Growth ◽  
Historical Time ◽  
Human Errors ◽  
Data Points

For the last 22 years I have been fitting logistic S-curves to data points of historical time series at an average rate of about 2–3 per day. This amounts to something between 15,000 and 20,000 fits. Combined with the 40,000 fits of the Monte Carlo study we did with Alain Debecker to quantify the uncertainties in logistic fits [1], probably qualifies me for an entry in the Guinness Book of Records as the man who carried out the greatest number of logistic fits.It hasn't all been fun and games. There have also been blood and tears and not only from human errors. There have been what I came to recognize as “misbehaviors” of reality. I have seen cases where an excellent fit and ensuing forecast were invalidated by later data. But well-established logistic growth reflects the action of a natural law. A disproved forecast is tantamount to violating this law. A law that becomes violated is not much of a law. What is going on? There is something here that needs to be sorted out.

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