Essential guide to time series feature engineering and forecasting — Time series forecasting involves model building on historical time-stamped data values and external factors to make scientific predictions that drive future strategic decision-making. Training a robust time-series forecasting model for accurate and reliable predictions is one of the most challenging tasks, given its direct impact on related decisions. …

Essential guide to an open-source Python framework for automated feature engineering — Feature engineering is an important and time-consuming component of the data science model development pipeline. The feature engineering pipeline decides the robustness and performance of the model. There are various automated feature engineering packages that process and create features for a single dataset. But these packages fail for the use-cases…

An open-source package for time series preprocessing and forecasting with unified and user-friendly APIs — Time series forecasting involves model building on historical time-stamped data to make scientific predictions and drive future strategic decision-making. Time series forecasting has many uses in various domains including: Predict consumer demand for every product Forecasting pandemic spread, diagnosis, medication, and planning in healthcare

Extract hundreds of relevant features for your time series use-case — Time Series data capture the variable's value repeatedly over time resulting in a series of data points indexed in time order. In time series data has natural temporal ordering i.e. the value of a variable at a particular time is dependent on past values. Traditional machine learning algorithms are not…

Implementation of Exploratory Data Analysis libraries with a few lines of Python code — Exploratory Data Analysis is one of the popular components of a data science model development pipeline. A data scientist spends most of their time performing EDA to generate insights about the data. Automated EDA packages can perform EDA in a few lines of Python code. In this article, we will…

General pitfalls of interpretable machine learning — Interpretable machine learning techniques are becoming more popular among the data science community as more and more complex machine learning algorithms are adopted which are not easily interpretable. Model-Agnostic Interpretation techniques do not care about the underlying models, but they have the capability to interpret the model and provide insightful…

Essential guide to LSAN: Modeling Long-term dependencies and Short-term correlations with Hierarchical Attention — Electronic Health Records (EHR) are comprehensive historical health records that contain the symptoms of a patient when he/she visits a doctor. EHR data has a two-level hierarchical structure that consists of a set of time-ordered visits, and within each visit, there is a set of unordered diagnosis codes. …

Essential guide to time series analysis — The real-world data often contain missing values. All types of the dataset including time-series data have the problem with missing values. The cause of missing values can be data corruption or failure to record data at any given time. In one of my previous articles, I have discussed 7 different…

Text data augmentation for NLP tasks — Training on a small sample of data increases the chances of overfitting. Data augmentation is a technique to create artificial similar samples of existing data. Data augmentation techniques are often used for tasks where the model expects a large amount of data, but we have limited access to the data…