When trend and seasonality is present in a time series, instead of decomposing it manually to fit an ARMA model using the Box Jenkins method, another very popular method is to use the seasonal autoregressive integrated moving average (SARIMA) model which is a generalization of an ARMA model. SARIMA models are denoted SARIMA(p,d,q)(P,D,Q)[S], where S refers to the number of periods in each season, d is the degree of differencing (the number of times the…

Added by Kostas Hatalis on April 12, 2018 at 10:30am — 1 Comment

This is an AI related post on the nature and philosophy of intelligence. In the various fields that study the mind, human or otherwise, there are many definitions (and lack of) for the term 'intelligence'. What is it, how can we measure it, how can we reproduce it? What implications does this have in the fields of AI, machine learning, and data science? A paper [1] by Shane Legg and Marcus Hutter, attempted to survey the definition from these various fields. The following are some sample…

ContinueAdded by Kostas Hatalis on March 15, 2018 at 12:30pm — No Comments

The use of formal statistical methods to analyse quantitative data in data science has increased considerably over the last few years. One such approach, **Bayesian Decision Theory (BDT)**, also known as Bayesian Hypothesis Testing and Bayesian inference, is a fundamental statistical approach that quantifies the tradeoffs between various decisions using…

Added by Kostas Hatalis on March 15, 2018 at 12:00pm — No Comments

Fish schools, bird flocks, and bee swarms. These combinations of real-time biological systems can blend knowledge, exploration, and exploitation to unify intelligence and solve problems more efficiently. There’s no centralized control. These simple agents interact locally, within their environment, and new behaviors emerge from the group as a whole. In the world of…

ContinueAdded by Kostas Hatalis on March 15, 2018 at 12:00pm — 2 Comments

The majority of industry and academic numeric predictive projects deal with deterministic or **point forecasts** of expected values of a random variable given some conditional information. In some cases, these predictions are enough for decision making. However, these predictions don’t say much about the uncertainty of your underlying stochastic process. A common desire of all data scientists is to make predictions for an uncertain future. Clearly then, forecasts should…

Added by Kostas Hatalis on March 15, 2018 at 12:00pm — No Comments

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