The broad goal for time series analysis is to be able to build a model that identifies all autocorrelation in a time series and uses this to create trend forecasts. We want to be able to use all the information from past behaviour that we believe is relevant to future movements with the hope that all is left is to add some form of uncorrelated error function that is derived from a common distribution. Whilst, especially in the financial markets, this level of accuracy is more an optimal/un-reachable solution than an attainable one due to the many other factors at play, the principle goal is still something we can work towards. With time series analysis, we alter the above to ask the following question -> how much of past behaviour can we use to model future prices and what
Introducing Random Walk Models
Introducing Random Walk Models
Introducing Random Walk Models
The broad goal for time series analysis is to be able to build a model that identifies all autocorrelation in a time series and uses this to create trend forecasts. We want to be able to use all the information from past behaviour that we believe is relevant to future movements with the hope that all is left is to add some form of uncorrelated error function that is derived from a common distribution. Whilst, especially in the financial markets, this level of accuracy is more an optimal/un-reachable solution than an attainable one due to the many other factors at play, the principle goal is still something we can work towards. With time series analysis, we alter the above to ask the following question -> how much of past behaviour can we use to model future prices and what