Autoencoders and methods like ACF and PACF effectively identify seasonality in time series data, enhancing business forecasting.

Data preprocessing refines raw data for accurate analysis by handling missing values, normalizing and processing data.

Stationarity, crucial for reliable time series analysis, is confirmed through tests like ADF and KPSS, facilitating easier modeling and interpretation.

The article uses a mock AirPassengers dataset to visually demonstrate trends and seasonal patterns in the airline industry.

Why and how to clean data

Compare, k-means, DBSCAN and Hierarchical Clustering

Learn how Fastcluster, Apache Spark, and GPU-accelerated solutions can help.

Sift through the noise and categorize datasets into actionable segments

Learn how to prevent overfitting from impacting your model.

Learn how to use autoencoders which are a class of artificial neural networks for data compression and reconstruction.

Learn to ensure the validity, reliability, and accuracy of your model.

Learn how to describe, summarize, and find patterns in the data from a single variable.