Every organization now generates time-stamped data. Sales figures, website traffic, inventory levels, customer behavior—all flowing in as sequences across time. The natural response is to forecast: what happens next?

But forecasting methods have proliferated wildly. Exponential smoothing, ARIMA, Prophet, LSTM networks, gradient boosting, transformer architectures. Each comes with advocates claiming superior performance. The result is decision paralysis—or worse, the assumption that newer and more complex automatically means better.

The reality is more nuanced and more useful. Different forecasting situations call for different methods. The skill isn't mastering every technique; it's recognizing which technique matches your specific data characteristics and business requirements. Get that matching right, and even simple methods deliver remarkable accuracy. Get it wrong, and sophisticated neural networks produce expensive garbage.

Before selecting any forecasting method, you need to understand what your data is actually doing. Time series have three fundamental components: trend (long-term direction), seasonality (predictable cycles tied to calendar periods), and cycles (irregular fluctuations driven by business or economic conditions).

Decomposition is your first diagnostic tool. Plot your series and visually identify these components. Is there consistent upward or downward drift? Do you see regular spikes every December, every Monday, every quarter? Are there irregular waves that don't align with the calendar? Many practitioners skip this step and jump straight to modeling. That's like prescribing medication without examining the patient.

The strength of these components matters as much as their presence. A series dominated by strong seasonality with minimal noise calls for seasonal methods. A series with weak patterns and high volatility suggests that sophisticated models may just be fitting noise. Calculate the ratio of signal to noise using variance decomposition. If your seasonal and trend components explain less than 30% of total variance, temper your accuracy expectations regardless of method.

Also examine how these components interact. Additive patterns maintain constant amplitude—seasonal peaks stay the same size as the level changes. Multiplicative patterns scale proportionally—seasonal effects grow larger as the overall level increases. This distinction directly determines which model variants to use. Retail sales typically show multiplicative seasonality: holiday spikes get bigger as the business grows. Temperature data shows additive seasonality: summer-winter differences stay roughly constant year over year.

Takeaway

The choice of forecasting method is downstream of understanding your data's structure. Pattern diagnosis should consume more time than model selection.

The most counterintuitive finding in forecasting research is how often simple methods win. In the M3 and M4 forecasting competitions—the most rigorous benchmarks available—simple exponential smoothing and its variants consistently outperformed more complex alternatives across thousands of real-world series.

This isn't an argument against complexity. It's an argument for appropriate complexity. The decision tree works like this: Start with exponential smoothing methods (Holt-Winters for seasonal data, simple exponential smoothing otherwise). These methods adapt quickly to recent changes and require minimal historical data—often just two or three years. They excel when patterns are stable and forecasting horizons are short.

Move to ARIMA-class models when you need to capture more complex autocorrelation structures—when today's value depends on values from multiple past periods in non-obvious ways. ARIMA also handles irregular intervals and can incorporate external regressors. But ARIMA requires more data, more expertise to specify correctly, and can overfit badly if you're not careful with model selection criteria.

Neural approaches—LSTMs, temporal convolutional networks, transformers—earn their place with high-frequency data, complex multi-step-ahead forecasts, or when you have massive training data across similar series (think Amazon forecasting millions of products). Below 500 observations, neural methods rarely justify their complexity. They're also computationally expensive and harder to explain to stakeholders. The question isn't whether neural networks can forecast—they can. The question is whether they forecast better than simpler alternatives given your specific constraints.

Takeaway

Complex models earn the right to be deployed only when simpler alternatives demonstrably fail on your specific data and forecasting horizon.

Here's what decades of forecasting research consistently shows: combined forecasts outperform individual forecasts. Not sometimes. Reliably. This is one of the most robust findings in the field, yet many organizations still bet everything on a single model.

The simplest combination—taking the arithmetic mean of two or three different methods—reduces forecast error by averaging out their individual biases. More sophisticated approaches weight methods based on recent performance or use machine learning to learn optimal combinations. But even naive averaging captures most of the benefit.

Combinations also provide something individual forecasts cannot: honest uncertainty quantification. When your exponential smoothing and ARIMA forecasts diverge significantly, that disagreement tells you something important. It signals periods of higher uncertainty that should inform inventory buffers, staffing flexibility, or hedging strategies. A single point forecast, no matter how sophisticated, hides this information.

Practically, build your ensemble from methods with different assumptions and failure modes. Combine a method that responds quickly to recent changes (exponential smoothing) with one that captures longer-term patterns (ARIMA) and perhaps one that handles nonlinearity (gradient boosting or a simple neural network). The goal isn't methodological diversity for its own sake—it's ensuring that when one approach fails, others compensate. Production forecasting systems at companies like Uber, Amazon, and Walmart all rely on ensemble approaches, continuously learning which components perform best under different conditions.

Takeaway

Forecast combination is not hedging your bets—it's systematically exploiting the fact that different methods fail in different ways.

Method selection in forecasting is ultimately a resource allocation problem. You have limited data, limited expertise, limited computational resources, and limited time to explain results to decision-makers. The best forecasters match method complexity to what these constraints allow.

Start by understanding your data's signature patterns. Test simple methods first and only add complexity when it demonstrably improves out-of-sample accuracy. Build ensembles that combine complementary approaches and use their disagreement to quantify uncertainty.

The goal isn't the most sophisticated forecast. It's the most useful forecast—one that's accurate enough, explainable enough, and delivered fast enough to actually improve decisions.