# Time Series Split Validation When Certain Time Display Unique Characteristics

Since my data is a time series, I've been using an expanding window walk forward validation via Sklearn's TimeSeriesSplit() to tune the hyper-parameters of my NN. However, I've realized that certain months of my data display characteristics unique to that specific month (e.g, consistent spikes at 3 p.m only in February and April, or a massive change during the middle of November), yet I only have data going back a year and 1 month. Therefore when I train on a subset of my data, the model may not see these special patterns. Is there a better way to split my data for training and validation?