Detection of Causal Relationships Based on Residual Analysis


The detection of causal interactions between variables from time series knowledge is a crucial problem in several research areas. Granger causality may be a well-known approach that uses prediction error to infer causality. However, the autoregressive models fitted to information usually do not pass model validation tests based mostly on residual analysis, resulting in low causality values that can be inconclusive. The strategy proposed here fits models for paired combination of all variables and inferences regarding causality are provided when performing residual analysis. The model order is increased until the autocorrelation take a look at of residual and cross-correlation check of residuals and input offer a solution about causality. The thresholds to come to a decision the existence of causality are provided directly by the information. Higher order multivariate systems are equally considered and a test to check if causality is direct or indirect is also proposed. The utility of the proposed approach is illustrated by many examples including application on a simulated data set and routine operating knowledge from business for causality analysis.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : A Supervised Machine Learning Algorithm for Heart Rate Detection Using Doppler Motion-Sensing Radar ABSTRACT: The development of vital sign radar technology has shown to be an effective tool for measuring various
PROJECT TITLE : Accurate and Robust Video Saliency Detection via Self-Paced Diffusion ABSTRACT: In order to estimate video saliency in the short term, traditional video saliency detection algorithms usually follow the common
PROJECT TITLE : Alzheimers Diseases Detection by Using Deep Learning Algorithms ABSTRACT: Accurate Alzheimer's disease (AD) diagnosis is critical for patient treatment, especially in the early stages of the disease, because
PROJECT TITLE : An Explainable Machine Learning Framework for Intrusion Detection Systems ABSTRACT: Machine learning-based intrusion detection systems (IDSs) have proven to be useful in recent years; in particular, deep neural
PROJECT TITLE : Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1- Constraint ABSTRACT: Autonomous vehicle technology relies heavily on traffic sign detection. Researchers

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry