Improving Forecast Accuracy with Time Series Decomposition in Mobility Analytics

Accurate forecasting is essential for businesses managing telecom expenses. Whether you're tracking smartphone data usage or IoT connectivity patterns, understanding what drives your mobility data - and how - is the first step toward selecting the right forecasting model.

A core challenge is that mobility data is rarely straightforward. It often includes:

• Long-term growth trends

• Seasonal usage spikes

• Unexpected noise like network outages or promotional surges

This is where decomposition comes in.

What is Time Series Decomposition?

Decomposition breaks a time series into three components:

• Trend - The long-term direction of usage (e.g., steady increase in IoT devices)

• Seasonality - Repeating, predictable patterns (e.g., higher usage during holidays)

• Residual (Noise) - Irregular variations due to one-off events

By isolating these components, you gain clarity on what's driving changes. This makes it easier to:

• Understand historical behavior

• Identify anomalies

• Select the right forecasting method

Here's what a decomposition might look like:

Choosing the Right Forecasting Model

Once you've decomposed your data, you can better match forecasting models to patterns in your data. Here's a simple guide:

Situation	Recommended Model	Notes
Clear trend, no seasonality	Holt's Linear Trend	Captures upward/downward trends
Trend + seasonality	Holt-Winters (Triple Exponential Smoothing)	Works well with additive or multiplicative seasonality
Complex patterns, autocorrelation	ARIMA/SARIMA	Power, but needs tuning
Recent values most important	Simple Moving Average	Very basic, short horizon
Deep learning or large-scale data	Prophet or LSTM	For more advanced needs

Case Example: Holt-Winters Forecasting

In our sample mobility dataset, we observed both trend and seasonal effects - making Holt-Winters Exponential Smoothing the ideal choice. It's:

• Capable of modeling trend and seasonality

• Easy to interpret and deploy

• Well-suited for short- to medium-term forecasting

Here's how the model performs:

Conclusion

Decomposition isn't optional - it's essential.

Without it, you're guessing which model to use. With it, you understand:

• How your data behaves

• What drives changes

• What model best fits your needs

Taking the time to decompose your mobility data enables smarter forecasting and better business decisions. Whether it's smartphones or IoT traffic, understanding trend, seasonality, and noise is the foundation of predictive accuracy.