13. Feature Engineering#
Feature engineering plays a crucial role in time series analysis and can greatly impact the performance of deep learning models. Here are some techniques for feature engineering using deep learning in time series:
Lagging Variables: Creating lagged versions of the input variables can provide historical context to the model. For example, including lagged values of a variable as additional input features can help the model capture temporal dependencies and patterns.
Rolling Window Statistics: Calculating statistical measures such as mean, standard deviation, minimum, maximum, or percentiles over a rolling window can capture trends, seasonality, and other patterns in the data. These statistics can be used as additional features to provide contextual information.
Time-based Features: Extracting time-related features from the timestamps can be useful, such as hour of the day, day of the week, month, season, or public holidays. These features can help capture recurring patterns or specific temporal behavior.
Fourier Transforms: Applying Fourier transforms to the time series data can identify dominant frequencies and periodic components. The resulting frequency domain features can be used as inputs to the deep learning model.
Wavelet Transforms: Similar to Fourier transforms, wavelet transforms decompose the time series into different frequency components. Wavelet coefficients at different scales and levels can be used as features.
Trend and Seasonality Decomposition: Decomposing the time series into trend, seasonality, and residual components using techniques like moving averages or decomposition methods (e.g., STL decomposition) can provide additional features that capture different aspects of the data.
Recurrence Plots: Recurrence plots visualize the patterns of recurrence in the time series data. Extracting features from these plots, such as recurrence quantification analysis measures, can capture nonlinear dynamics and complex relationships.
Domain-Specific Features: Depending on the specific domain or application, you can engineer features that are relevant to the problem at hand. For example, in financial time series analysis, features like moving averages, volatility measures, or technical indicators can be useful.
It’s important to note that not all of these techniques may be applicable or beneficial for every time series problem. The choice of feature engineering techniques depends on the nature of the data, the problem you are trying to solve, and the characteristics you want the model to capture.
Deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), or transformers, can then be trained on these engineered features to learn complex patterns and relationships in the time series data.