How to construct dev set for time series data

When working with time series data, the concepts of training set and development set (also referred to as validation set) take on a slightly different meaning compared to traditional machine learning tasks. Let's explore how these sets are used in the context of time series data.

Training Set for Time Series Data: The training set in time series analysis consists of historical observations or measurements collected over a specific time period. The training set serves as the foundation for the model to learn patterns, trends, and dependencies within the data. It typically spans a significant period to capture the temporal nature of the data and provide sufficient information for the model to learn from.

When constructing the training set for time series data, it is essential to maintain the temporal order of the observations. This means that the data points are arranged chronologically, with earlier observations preceding the later ones. This temporal ordering is crucial because time series data often exhibits trends, seasonality, and other temporal patterns that the model needs to capture accurately.

Development Set for Time Series Data: The development set, also known as the validation set or holdout set, is a portion of the time series data that is used to evaluate the model's performance during training and hyperparameter tuning. It plays a critical role in assessing how well the model generalizes to unseen data and helps prevent overfitting.

To construct the development set for time series data, a contiguous segment of data following the training set is set aside. This segment typically represents a more recent time period compared to the training set, ensuring that the evaluation captures the model's ability to make predictions on the most up-to-date data.

Unlike in traditional machine learning tasks, random shuffling or splitting the data into disjoint subsets is not suitable for time series data. This is because the temporal order of the data is crucial for accurately modeling and forecasting future observations. Therefore, the development set is usually a consecutive sequence of observations following the training set, without overlapping with it.