Time Series Forecasting for Predictive Maintenance: Methods, Trade-offs, and Open Challenges

Predictive maintenance depends on forecasting: identifying, from historical sensor data, when a piece of equipment is likely to fail before it does. The forecasting techniques used for this — ranging from decades-old statistical methods to modern deep learning architectures — make different trade-offs between accuracy, interpretability, and the volume of data required, and the right choice depends heavily on the characteristics of the data itself.

§ 02What makes a time series tractable

Two distinctions shape which forecasting method will work well. The first is stationary versus non-stationary data: stationary series have a constant mean and variance over time, while non-stationary series show trends or seasonality that need to be modelled explicitly. The second is univariate versus multivariate: a single sensor reading over time versus multiple interacting variables — vibration, temperature, pressure — that together describe equipment condition. Most industrial predictive maintenance problems are multivariate and non-stationary, which immediately rules out the simplest forecasting approaches.

§ 03From statistical models to deep learning

ARIMA and its seasonal variant SARIMA remain effective for short-term forecasting on stationary or seasonally-patterned data, and exponential smoothing handles trend and seasonality with relatively little tuning. Where relationships in the data are non-linear — which is common in industrial settings — machine learning models such as random forests and support vector machines tend to outperform classical statistical methods. For data with long-term dependencies, where a failure signature only becomes visible across an extended window, deep learning architectures like LSTM and GRU networks are better suited, at the cost of requiring substantially more data and compute.

§ 04The practical constraints

Model choice on paper rarely survives contact with real deployment constraints. Accurate forecasting requires high-quality, consistent data over a long period — incomplete or noisy sensor data degrades every method, but degrades deep learning models least gracefully since they have the least built-in structure to fall back on. Non-stationary data requires either preprocessing or models designed to handle it directly. At industrial scale, the volume of incoming sensor data can make real-time inference computationally expensive. And deep learning models in particular operate as black boxes, which is a genuine obstacle in maintenance contexts where engineers need to understand why a model is flagging a component, not just that it is.

§ 05Where the field is heading

Several trends are shaping how these trade-offs get resolved in practice. The growth of IoT sensing has made multivariate, high-frequency data the norm rather than the exception, pushing demand toward models that scale. AutoML platforms are lowering the expertise barrier to deploying time series models without deep statistical training. Hybrid approaches — combining statistical methods for interpretability with machine learning for accuracy — are increasingly common rather than treating the choice as binary. And edge computing is moving inference closer to where sensor data is generated, reducing the latency between a developing fault and a flagged alert.