Cerliani

Hacking Granger Causality Test with ML ApproachesPhoto by Phoenix Han on UnsplashIn time series forecasting is often helpful to inspect graphically the data at disposal. This helps us understand the dynamics of the phenomena we are analyzing and take decisions accordingly. Despite having a colorful plot with our time series may be fascinating, it may lead to incorrect conclusions. Time series are tricky because often unrelated events may still be visually seen to be related.An example of spurious correlation As rational…

Test Effectiveness of any Treatment over Time with PCAPhoto by Raul Petri on UnsplashThe standard, presented in the literature and adopted at large scale by companies, to study the causal impacts of business actions (like design change, discount offers, and clinical trials) is for sure AB testing. When carrying out an AB test, we are doing a randomized experiment. In other words, we randomly split a population under our control (patients, users, customers) into two sets: a treatment and a control group. The treatment…

Are you Searching Parameters Efficiently?Photo by Mpho Mojapelo on UnsplashMost data scientists, who attend meetings to present ML results to business stakeholders, usually answer questions like these:AUC? What is it? Could you please elaborate?Terms and concepts standard in data science daily routine may be unfamiliar to most. This frequently happens when artificial intelligence products are developed to solve real-world problems. In this scenario, data scientists work together and collaborate with domain experts to…

Test Statistical Significance in any Context Without AssumptionsPhoto by Christian Stahl on UnsplashThe importance of data analytics is well-known in every field. From business to academics, carrying out proper analysis is the key to reaching cutting-edge results. In this sense, it is crucial to correctly manipulate and extract meaningful insights from the data at our disposal. Data Analysts/Scientists are responsible to fill the gap between theoretical hypothesis and practical evidence.Providing an analytical answer to…

Accurate Uncertainty Quantification with MAPIE and TSPIRALPhoto by Lucas George Wendt on UnsplashWhen carrying out a time series forecasting task we are used to developing solutions that produce point-wise estimations of future observations. That’s correct and, if properly validated, they may positively impact business results. Is it possible to do better? Can we provide more detailed forecasts by simply adding further information?Enriching our forecasts with prediction intervals is the key. Practically speaking a…

Time to Event Forecasting with Simple ML ApproachesPhoto by Markus Spiske on UnsplashIn data-driven companies, time-to-event applications assume a crucial role in decision-making (also more than we can imagine). With time-to-event analysis, we are referring to all the techniques used to measure the time which elapses until some events of interest happen. This straightforward definition may immediately outline all the benefits of developing time-event applications in business contexts (and not only).Time-to-event origins…

Studying Events Frequency to Identify Unusual BehaviorsPhoto by Isravel Raj on UnsplashNowadays we live in a data-centric world. With every action, we generate a significant amount of data that can be collected and used to produce valuable business insights. Big tech companies know these dynamics very well. By monitoring our daily activities, it’s possible to identify our habits and preferences to customize offers and increase the probability of engagements.Accessing, and leveraging at the same time, the huge variety of…

Comparing Alternatives to Handle Missing Values in Time SeriesPhoto by Kiryl Sharkouski on UnsplashHaving at disposal clean and easily understandable data is a dream for every data scientist. Unfortunately, the reality it’s not so sweet. We have to spend a good part of our time committed to carrying out data exploration and cleaning. However, a good explorative analysis it’s the key to extracting to most useful insights and producing better outcomes.In the context of a predictive application, a detailed overview of the…

Incorporate into Prediction Intervals both Aleatoric and Epistemic UncertaintiesPhoto by engin akyurt on UnsplashUsually, applications in the machine learning industry don’t consider how to produce uncertainty estimates. In many real-world tasks, it’s not only required to make accurate predictions. Providing a confidence score on the model outcomes may be crucial to making the most effective decisions.Except for some deep learning techniques or other special cases, producing confidence estimates is not a free lunch. We…

Beyond PCA: A Graph-based Approach to Detect Anomalous PatternsPhoto by Alain Pham on UnsplashWhen working on an anomaly detection task, we are used to discovering and pointing out situations where the data register unseen dynamics. The ability to study the past and extrapolate a “normal” behavior is crucial for the success of most anomaly detection applications. In this situation, an adequate learning strategy must take into consideration the temporal dependency. What in the past may be considered “anomalous”, now may be…