Time series forecasting is an important task in machine learning and is frequently used in various fields such as finance, manufacturing, healthcare, and natural sciences. Google researchers introduced a unique decoder model for the task, called TimeFM, based on pre-training a patched decoder-style attention model on a large time series corpus comprising synthetic and real-world data sets. Time series data, collected at regular intervals over time, plays a crucial role in predicting future values. Traditional methods such as ARIMA and GARCH have been widely used. Recent advances in deep learning, particularly in large language models (LLM) for natural language processing (NLP), have opened up new ways for researchers to handle time series forecasting by applying these models to the task.
Existing deep learning models such as DeepAR, Temporal Convolutions, and NBEATS are popular for time series forecasting and outperform traditional statistical methods. Recently there has been work on reusing or fine-tuning large language models (LLM) such as GPT-3 and LLaMA-2 for time series forecasting. In the paper, the researchers aim to investigate whether a model pre-trained on massive amounts of time series data can learn useful temporal patterns for making accurate forecasts on never-before-seen data sets.
The TimesFM architecture involves a stacked transformer with a patched decoder-style attention mechanism inspired by the successful patch-based modeling in long-term forecasting. The proposed model uses decoder-only training, allowing the model to predict the future by viewing different numbers of input patches in parallel. Data for training includes synthetic and real-world data. Real-world data is taken from various sources such as Google Trends and Wiki Pageviews, while synthetic data is generated from statistical models such as ARIMA.
Experiments show that TimesFM achieves impressive zero-forecast performance. Not only is the performance of the model impressive, but it is also more efficient than existing models in terms of parameter size and pre-training data. The model is evaluated on public datasets from Darts, Monash and Informer, demonstrating its ability to generalize and outperform specialized baselines.
Based on a large corpus of synthetic and real-world data, TimesFM is an innovative time series foundation model. The model's unique architecture, including a patched decoder attention mechanism and decoder-only training, contributes to its strong zero-shot forecasting performance. TimesFM's ability to outperform baselines on multiple data sets demonstrates the potential of large pre-trained models for time series forecasting, providing a promising avenue to reduce training data and computational requirements in this field.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
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