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Time Series Seasonality Detection

3 posts · Dec 2023

What this trail covers

Hex enables users to detect seasonality in time series data using autocorrelation, ACF, and PACF. This includes understanding autocorrelation basics, calculating and interpreting autocorrelation values, and applying these techniques to real-world datasets like air passenger data. The platform supports visual inspection via line plots and seasonal decomposition to identify trends, seasonal patterns, and residuals, aiding in business forecasting and decision-making.

All Hex trails
  1. Detecting Seasonality Through AutocorrelationThis post introduces and details methods for detecting seasonality in time series data, focusing on autocorrelation, ACF, and PACF. It explains the calculation and interpretation of autocorrelation values, including positive, negative, and zero correlations, as well as peak and multiple peaks. The post demonstrates these concepts using air passenger data, illustrating seasonality detection through line plots and seasonal decomposition (trend, seasonal, residual components). It provides Python code examples using pandas, matplotlib, and statsmodels, and links to a Hex notebook for hands-on exploration.
  2. Understanding the Importance of Stationarity in Time SeriesThis post details the importance of stationarity in time series analysis, explaining strict and weak stationarity, and their key properties. It highlights why stationarity is crucial for time series modeling and forecasting. The post also introduces and demonstrates the Augmented Dickey-Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for assessing stationarity, using the 'Air Passengers' dataset as an example.
  3. Unveiling Patterns Using Time Series VisualizationThis post details techniques for visualizing time series data to uncover patterns like trends and seasonality. It demonstrates using line charts, area charts, heat maps, histograms, and box plots. It also shows how to decompose a time series into trend, seasonal, and residual components using `statsmodels.tsa.seasonal.seasonal_decompose` and how to visualize the average seasonal component by month. The post uses a mock AirPassengers dataset and provides Python code examples for data generation and visualization.