[- forecasting methods]
b-1) TIME SERIES ANALYSISwikipedia.com - "in statistics, signal processing, and many other fields, a time series is a sequence of data points, measured typically at successive times, spaced at (often uniform) time intervals. Time series analysis comprises methods that attempt to understand such time series"
there are many models specifically designed to maximised time series, that is, to understand the implicit behaviour and, hence, to predict future events on the basis of this information
main classification
1. linear dependence [~ linear regressions]
three main types
- autoregressive (AR) models
- integrated (I) models
- moving average (MA) models
over these ones, there are still two combinations [autoregressive moving average (ARMA) models & autoregressive integrating moving average (ARIMA) models] & one generalisation [autoregressive fractionally integrated moving average (ARFIMA) models] of them
2. non-linear dependence or autoregressive conditional heteroskedasticity models
[~ non-linear regressions]
- generalised autoregressive conditional heteroskedacity [GARCH] models
- threshold autoregressive conditional heteroskedacity [TARCH] models
- exponential generalised autoregressive conditional heteroskedacity [EGARCH] models
...
all these models have two characteristics in common
a. account for just two variables [dependent vs. independent]
b. try to understand stochastic [= random] processes
main classification
1. linear dependence [~ linear regressions]
three main types
- autoregressive (AR) models
- integrated (I) models
- moving average (MA) models
over these ones, there are still two combinations [autoregressive moving average (ARMA) models & autoregressive integrating moving average (ARIMA) models] & one generalisation [autoregressive fractionally integrated moving average (ARFIMA) models] of them
2. non-linear dependence or autoregressive conditional heteroskedasticity models
[~ non-linear regressions]
- generalised autoregressive conditional heteroskedacity [GARCH] models
- threshold autoregressive conditional heteroskedacity [TARCH] models
- exponential generalised autoregressive conditional heteroskedacity [EGARCH] models
...
all these models have two characteristics in common
a. account for just two variables [dependent vs. independent]
b. try to understand stochastic [= random] processes
trendingBot point of view
statitstics' answer -> random essence has to be accounted for (?)
b. stochastic/random ~ impossible to be predicted - ... then?
b.2.- a weighted (based on sensible assumptions) regression method shouldn´t be defined as stochastic, if the weightages are applied on a regular and consistent basis
b.3.- probably, the randomness might be removed, in case a more adequate set of variables would be chosen
CONCLUSION 1 time series analysing methods can be defined as extensions of conventional regression methods to stochastic behavioursb.3.- probably, the randomness might be removed, in case a more adequate set of variables would be chosen
CONCLUSION 2 trendingBot's result for any (stochastic) time series = "trend not found"
