Multi-dimensional Time Series Analysis and OLAP methods are important, when working with Time Series Data. Often multi-dimensional Time Series Analysis as term is referred to is a complete set of methods in applying machine learning in forms of forecasts or searching for anomalies and patterns. In this article we focus on good old deterministic multi-dimensional Time Series Analysis foundations to prepare, investigate and aggregate the Time Series Data in a deterministic way. Knowing these multi-dimensional Time Series Analysis foundations is essential, because at least 80% of Data Science work is Big Data and Big Data Landscape preparation. Common multi-dimensional analysis operations get applied in Business Intelligence and Data Warehousing where they are often called Online AnaLytical Processing (OLAP) operations [1]. In this article, we discuss and describe what the most important multi-dimensional Time Series Analysis and OLAP methods are and show examples of how the different operations are applied on a Time Series Data sets. In the beginning, we talk about OLAP in Data Warehouse landscapes and Time Series Data processing in Big Data landscapes. Subsequently, we give some insights into why and to whom multi-dimensional time series analysis with OLAP matters within an enterprise.

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variable-lag Granger causality and Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring both variable-lag Granger causality and Transfer Entropy relations. We demonstrate our approach on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approach can be applied in any domain of time series analysis. The software of this work is available in the R package: VLTimeSeriesCausality.

I used this example in my talk at useR!2019 in Toulouse, and it is also the basis of a vignette in the package, and a recent blog post by Mitchell O'Hara-Wild. The data set contains domestic tourist visitor nights in Australia, disaggregated by State, Region and Purpose. An example of a feature would be the autocorrelation function at lag 1 -- it is a numerical summary capturing some aspect of the time series. Autocorrelations at other lags are also features, as are the autocorrelations of the first differenced series, or the seasonally differenced series, etc. Another example of a feature is the strength of seasonality of a time series, as measured by \(1-\text{Var}(R_t)/\text{Var}(S_t R_t)\) where \(S_t\) is the seasonal component and \(R_t\) is the remainder component in an STL decomposition.

"Time series models are used to forecast future events based on previous events that have been observed (and data collected) at regular time intervals." We will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside. Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends. Time series forecasting is sometimes just the analysis of experts studying a field and offering their predictions.

Time Series is a class of data science problems where the primary values of interest are a series of data points measured over a period of time. This notebook aims to provide the basic building blocks of some of the more modern algorithms / techniques (and data!) for solving these types of problems. Is ARIMA the first thing you think of when you hear about time series? It might be time to explore other ventures and methodologies. There is a lot of new innovation and modern techniques being actively developed and some of them are outperforming the traditional ARIMA models.

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variable-lag Granger causality, a generalization of Granger causality that relaxes the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring variable-lag Granger causality relations. We demonstrate our approach on an application for studying coordinated collective behavior and show that it performs better than several existing methods in both simulated and real-world datasets. Our approach can be applied in any domain of time series analysis.

The purpose of this usecase is to formulate inferences and generate insights based on certain indicators like the bed occupancy status, ward-wise revenues, delivery procedures (normal or caesarian), revenues generated by doctors, etc. The analysis performed gave deep insights (yearly, monthly, weekly) into the number of outpatients getting converted to inpatients, ward-wise inputs on bed occupancy and revenues, doctors' performance and the revenues generated by them, type of delivery (normal or caesarian), etc. This usecase also tries to predict the weekly trends in the patients which will help for better monitoring and allotment of resources. To know how Qualetics gives an effective solution, download the full usecase.

Time series data are everywhere. This deliberately broad and multipurpose book can serve as either an introduction for the novice or a reference for someone looking to broaden a time series base. Unlike other texts specialized in time series analysis, this book includes many data munging and data sourcing tasks that are crucial to time series analysis. This book also takes a multidisciplinary approach in applying both statistical and machine learning methods to time series data. Finally, this book is multilingual and multi-topical in applying both R and Python to time series data across a wide variety of disciplines, from physics simulations to digital marking, and from blood glucose monitoring to open government data mining.

"Time series models are used to forecast future events based on previous events that have been observed (and data collected) at regular time intervals." We will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside. Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends.

Whether on-premises or in the cloud, your data provides a link to the past and a glimpse into the future. Why did you lose past customers? Which current customers should you pay more attention to? Where are your new customers going to come from? In this blog, I'll talk about the capabilities of Teradata Vantage and its Machine Learning Engine, and how it can help you turn 100% of your data into answers that your business can use to pave a path to the future.