Identification of Causal Dependencies in Multivariate Time Series

Telecommunications networks operate on enormous amount of time-series data, and often exhibit anomalous trends in their behaviour. This is caused due to increased latency and reduced throughput in the network which inevitably leads to poor customer experience [17]. One of the common problems in machine learning in the telecom domain is to predict anomalous behaviour ahead of time. Whilst this is a well-researched problem, there is far less work done in identifying causal structures from the temporal patterns of various Key Performance Indicators (KPI) in the telecom network. The ability to identify causal structures from anomalous behaviours would allow more effective intervention and generalisation of different environments and networks. The tutorial is focused on discussing existing frameworks for establishing causal discovery for time-series data sets. In this hands-on tutorial, we will be covering at least 3 state-of-the-art (SOTA) methods on causal time series analysis including Granger causality[8],convergent cross-mapping [4, 10, 15], Peter-Clark Momentary Conditional Independence (PC-MCI) [6, 14] and Temporal Causal discovery framework (TCDF)[11]. The need for a causation analysis[7], beyond correlation will also be explained using publicly available datasets, such as, double pendulum dataset [1]. The state-of-art methods are chosen to cover various aspects of the causal time series analysis, such as modelling the non-linearity (non-linear Granger Causality), attempting the problem from chaos and dynamic systems (CCM), information-theoretic approaches (PC-MCI, or having a data-driven approach (TCDF). State-of-the-art survey papers [2, 12] show that none of the methods can be said to be ideal for all the possible time series and there are relative advantages and shortcomings for each of these methods.