How are neural networks used for time series forecasting?

Neural networks are used for time series forecasting by learning patterns and dependencies in sequential data to predict future values. Unlike traditional statistical methods (e.g., ARIMA), neural networks can model complex, non-linear relationships and handle multivariate inputs. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are common choices because they process sequences step-by-step, maintaining a “memory” of past inputs. For example, an LSTM can capture trends and seasonality in hourly temperature data to predict the next day’s temperature. Convolutional Neural Networks (CNNs) are also used, applying filters over sliding windows of time to detect local patterns, such as sudden spikes in sales data.

Specific architectures are tailored for time series tasks. Transformers, originally designed for NLP, use self-attention to weigh the importance of different time steps, making them effective for long-range dependencies. For instance, a Transformer might predict electricity demand by focusing on weekly cycles or holiday anomalies. Hybrid models, like combining CNNs with LSTMs, use CNNs to extract features from raw data (e.g., sensor readings) and LSTMs to model temporal relationships. Autoregressive models, such as DeepAR, output probability distributions for probabilistic forecasts, which are useful in scenarios like retail inventory planning where uncertainty matters.

Practical implementation involves preprocessing data into fixed-length windows (e.g., using the past 30 days to predict the next 7). Libraries like TensorFlow and PyTorch provide tools for building these models. Key challenges include handling missing data, normalizing inputs, and avoiding overfitting with techniques like dropout. Metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) evaluate performance. For example, a developer might train an LSTM on stock price data, using early stopping to halt training if validation loss plateaus. Experimentation with architecture depth, input window size, and hyperparameters (e.g., learning rate) is often required to optimize results for specific datasets.