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.

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.

Evaluating the performance of Gulf of Alaska walleye pollock (Theragra chalcogramma) recruitment forecasting models using a Monte Carlo resampling strategy

2009 ◽  
Vol 66 (3) ◽  
pp. 367-381 ◽  
Author(s):  
Yong-Woo Lee ◽  
Bernard A. Megrey ◽  
S. Allen Macklin
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Gulf Of Alaska ◽  
Data Set ◽  
Forecasting Models ◽  
Walleye Pollock Theragra Chalcogramma ◽  
Sample Data ◽  
Out Of Sample ◽  
Data Points ◽  
Recruitment Model

Multiple linear regressions (MLRs), generalized additive models (GAMs), and artificial neural networks (ANNs) were compared as methods to forecast recruitment of Gulf of Alaska walleye pollock ( Theragra chalcogramma ). Each model, based on a conceptual model, was applied to a 41-year time series of recruitment, spawner biomass, and environmental covariates. A subset of the available time series, an in-sample data set consisting of 35 of the 41 data points, was used to fit an environment-dependent recruitment model. Influential covariates were identified through statistical variable selection methods to build the best explanatory recruitment model. An out-of-sample set of six data points was retained for model validation. We tested each model’s ability to forecast recruitment by applying them to an out-of-sample data set. For a more robust evaluation of forecast accuracy, models were tested with Monte Carlo resampling trials. The ANNs outperformed the other techniques during the model fitting process. For forecasting, the ANNs were not statistically different from MLRs or GAMs. The results indicated that more complex models tend to be more susceptible to an overparameterization problem. The procedures described in this study show promise for building and testing recruitment forecasting models for other fish species.

scholarly journals Data imputation in a short-run space-time series: A Bayesian approach

2017 ◽  
Vol 45 (5) ◽  
pp. 864-887
Author(s):  
Lars Pforte ◽  
Chris Brunsdon ◽  
Conor Cahalane ◽  
Martin Charlton
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Spatial Scales ◽  
Spatial Coherence ◽  
Data Imputation ◽  
The European Union ◽  
Short Run ◽  
Data Points

This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Carlo method in detail. Furthermore, we explain how we achieved spatial coherence between different time series and their observed and estimated data points.

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