Several types of ensemble techniques are available, ranging from very simple ones like weighted averaging or max voting to more complex ones like bagging, boosting and stacking. This blog post is an excellent starting point to get up to speed with the various techniques mentioned.
“N one of us is as strong as all of us”
Ensemble Techniques have become quite popular among the machine learning fraternity because of the simple fact that ‘one-size-fits-all’ can’t always practically hold good for individual models and there are almost always going to be some models which trade low variance for a high bias while others that do the opposite. The challenge is exacerbated for time series forecasting problems because even the best performing models may not perform consistently well throughout the forecast horizon. This is one of the motivations behind the topic of this article — the Arbitrated Dynamic Ensemble** (ADE)**. But more on that in a bit.
First, if you haven’t already, I’d recommend checking out this comprehensive guide to ensemble learning. Ensemble models are exactly that-an ensemble, or a collection of various base models (or weak learners as they are referred to in literature). Several weak learners combine to train a strong learner — as simple as that. How exactly they combine is what gives rise to the various types of ensemble techniques. Several types of ensemble techniques are available, ranging from very simple ones like weighted averaging or max voting to more complex ones like bagging, boosting and stacking.
This blog post is an excellent starting point to get up to speed with the various techniques mentioned.
Let us now look at two common ensemble learning techniques at a very high level. Weighted Average and *Stacking *are the building blocks of ADE so we will start by spending some time on these two ensemble techniques first.
This tutorial was supposed to be published last week. Except I couldn’t get a working (and decent) model ready in time to write an article about it.
In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting. In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.
While LSTMs have become increasingly popular for time series analysis, they do have limitations. Long-short term memory networks (LSTMs) are now frequently used for time series analysis.
Learn the Fundamental Rule of Time Series Analysis: Stationarity is an important concept in the field of time series analysis with tremendous influence on how the data is perceived and predicted.
Applying the ARIMA model to forecast time series dataThe notion of stationarity of a series is important for applying statistical forecasting models since.