The fastai library, built on top of PyTorch, is widely known for its state-of-the-art deep learning capabilities in fields such as computer vision and natural language processing. However, fastai also supports time series data analysis and processing, making it a powerful tool for handling temporal data in machine learning projects. In this blog post, we will explore the key features of fastai for time series data and demonstrate how to effectively leverage its functionalities.

Time series data and its challenges

Time series data represents a sequence of data points collected at regular intervals over time. Examples of time series data include stock prices, weather measurements, and sensor data. Analyzing and modeling time series data can be challenging due to several factors:

• Temporal dependencies: Time series data often exhibits temporal dependencies, where each data point is influenced by previous values. This makes traditional machine learning techniques less effective since they do not consider the sequential nature of the data.
• Variable length: Time series data may have variable lengths, which further complicates the task of modeling.
• Seasonality and trends: Time series data often contains periodic patterns, trends, and seasonal variations, which need to be captured to make accurate predictions.

Leveraging fastai for time series data

fastai provides several powerful tools and techniques to handle time series data effectively:

1. Time series specific data loaders

fastai offers specialized data loaders for time series data. The TSDataLoaders class helps in creating a time series dataloader with customizable options for features, targets, and batch sizes. This dataloader automatically handles padding or truncation of sequences to make them uniform in length, simplifying the training process.

2. Preprocessing and feature engineering

fastai provides a convenient API for preprocessing and feature engineering time series data. You can apply various transformations such as scaling, differencing, and logarithmic transformations using the TSTransform class. This class allows you to easily apply these operations to both training and validation data, ensuring consistent preprocessing across different datasets.

3. Time series specific models

fastai includes pre-built models specifically designed for time series analysis. These models, such as InceptionTime and TabTransformer, are equipped with mechanisms to capture temporal dependencies and patterns effectively. You can easily leverage these models and fine-tune them for your specific time series prediction task.

4. Temporal fusion transformers (TFTs)

fastai has added support for Temporal Fusion Transformers (TFTs), a powerful deep learning architecture for time series forecasting. TFTs can effectively capture long and short-term dependencies, handle missing data, and model complex interactions between features and time. fastai provides pre-trained TFT models and utilities to help you build and train TFTs for your specific time series forecasting problem.

Conclusion

fastai provides a comprehensive set of tools and techniques for time series data processing and model building. By leveraging fastai’s time series capabilities, you can effectively handle the challenges associated with time series data, such as temporal dependencies, variable lengths, and seasonal patterns. Whether you need to perform time series analysis, forecasting, or anomaly detection, fastai can greatly simplify your workflow and help you achieve accurate and powerful models.