Time series analysis is becoming more and more important in the world of big data and data mining, but without a time projection, it is impossible to analyze them. What our team realized was that the time series data management platforms on the market are based on architectures oriented to static and not dynamic problems. Trying to understand the behaviour of time-series data using current platforms is similar to watching a movie frame by frame, one every few seconds, making it impossible to grasp its storyline.

In this use case, we will explore how Shapelets accelerated platform can be used to improve price prediction. We will use historical energy price data together with energy generation data to find out how energy consumption and production are related.

What are Times Series

Concept

A time series is a collection of observations of a variable made sequentially over time, where the order of observation is important. The values of a time series are linked to instants of time (daily, monthly, yearly, etc.), so that the analysis of a series involves the joint handling of two variables; the variable under study itself and the time variable.

Time series predictions are an increasingly important field of Machine Learning that is often overlooked because many of the prediction problems are related to the time component.

Our algorithms make predictions about the future values of the time series. In general, the possible future values of a time series are calculated, taking into account all past observations equally. However, we focus on smaller time ranges to overcome the idea of “conceptual drift”. That is, using only the last year of observations instead of all available data. When we deal with sets of time series, the problem is different: time series add an explicit order dependence between observations.

With the current rise of electronic devices (urban monitoring sensors, smart power meters, cell phones) and virtual agents (e.g., Twitter feeds) that detect, monitor, and track people and environmental activity, these types of data are continuously transmitted and interconnected. Two defining characteristics of these datasets, which are applicable to both IoT (internet of things) and social networks, are the temporal attributes of the time series and the correlation that exists between them. These datasets that present these temporal and graphical characteristics have not been adequately studied from the perspective of scalable Big Data management and analysis, despite the importance of analyzing this data.

What are Times Series

Have I seen thisbefore? Pattern identification

This simple question is one of the key advantages and differences that Shapelets offers. With Shapelets algorithms, you will be able to identify patterns that you have seen before at a speed and precision unmatched by other tools in the market.

Unveil the power of patter reconition with: a summary of a time of secuence

This simple question is one of the key advantages and differences that Shapelets offers. With Shapelets algorithms, you will be able to identify patterns that you have seen before at a speed and precision unmatched by other tools in the market.

Unusuak patterns or anomalies

Identifying and tracking unusual patterns has to be part of any time series analytics program. Automatic anomaly detection with Shapelets can save you time and help you check against past patterns.

Unveil the power of patter reconition with: a summary of a time of secuence

This simple question is one of the key advantages and differences that Shapelets offers. With Shapelets algorithms, you will be able to identify patterns that you have seen before at a speed and precision unmatched by other tools in the market.