Decomposing Time Series for Seasonality

Time series decomposition is a statistical technique that breaks the time series into several separate features like trend, seasonality and residuals. This step is useful for eliciting the patterns and behaviors present in the data. The models can be decomposed in two ways: additive and multiplicative. This ecommerce analytics approach provides essential ecommerce insights for understanding business patterns.

Additive vs. Multiplicative Decomposition

Additive decomposition presumes the observed data are a combination of trend T_t, seasonal S_t, and residual R_t components:

y_t = T_t + S_t + R_t

This model is appropriate when the seasonality is more or less consistent over time and not proportional to the level of the trend. For instance, if the holiday sales of a retailer increase by a fixed amount per year regardless of overall growth, the additive model is suitable.

Multiplicative decomposition assumes the observed data can be expressed by the following product:

y_t = T_t × S_t × R_t

This is appropriate when seasonal effects increase proportionally with the trend (such as sales peaking at a certain time of year, increasing by a percentage), or seasonality has already been removed from the data.

Isolating Components

Applying decomposition techniques such as moving averages, Seasonal and Trend decomposition using Loess (STL), and exponential smoothing, we de-trend the:

• Trend component: is indicative of the long-term trend or broad direction of the series.
• Seasonal component: It helps modelling repeating short-term patterns such as weekly or annual cycles.
• High frequency component: A residual noise, irregular fluctuations or high frequency variation left after removal of seasonality and trend.

Evaluating Multiple Seasonal Cycles

Time series can have more than one season. For instance, an online store may experience weekly cycles for customer shopping behaviour or annual peaks based on holidays or seasons. Utilizing this with respect to the different seasonal cycles can help with better understanding in decomposition and forecasting through predictive analytics ecommerce methods.

How trivas.ai Improves Time Series Decomposition and Seasonality Detection

trivas.ai is an AI powered e-commerce analytics platform with advanced time series analysis abilities for multi-dimensional data. As a comprehensive ecommerce tool and ecommerce software solution, it has the following properties, which are helpful in decomposing and analyzing seasonality:

• Automated Pattern Detection: trivas.ai leverages machine learning to automatically segment and decompose trend, seasonality (e.g., day-of-week effects), various seasonal patterns at multiple levels without any human involvement.
• Multiple Seasonal Cycle Analysis: The system can support overlapping seasonal cycles such as weekly, monthly and annual to reveal the true underlying patterns of customer behavior and improve customer journey understanding.
• Real-Time Predictive Insights: Using sophisticated time series decomposition trivas.ai delivers up-to-the-moment predictions that adjust for trends and seasonality, helping guide inventory and marketing decisions through ecommerce data analytics.
• Interactive dashboards: Visualize decomposed components interactively, and gain a better understanding of how seasonal effects can relate to your business overall performance with ecommerce performance analytics.
• Actionable Recommendations: Beyond decomposition, trivas.ai provides AI-generated recommendation to maximise campaign and inventory performance for identified seasonality and residual pattern.

Leveraging this potent mix of AI and statistical methods, trivas.ai is a best-in-class offering for commerce companies looking to use time series decomposition to make smarter business decisions and scale. This analytics in ecommerce approach delivers actionable insights that drive better outcomes.

This post builds on the previous notion of time series decomposition for seasonality and shows how trivas.ai accelerates this effort through AI-based tools and intelligence.