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Foundational Models for Time-series Analysis

Introduction

Time-series analysis is a statistical technique used to analyze data points collected or recorded at specific time intervals. Unlike traditional data analysis, time-series methods focus on capturing temporal patterns, trends, seasonality, and cyclic behaviors in data. These insights help predict future values, enabling informed decision-making. Time-series analysis is widely employed in numerous industries due to its ability to leverage historical data for forecasting and strategic planning. Time-series analysis is widely used across industries for its predictive power. In finance, it supports stock price prediction and risk management. Retailers and e-commerce platforms leverage it for demand forecasting and inventory optimization, while healthcare uses it for monitoring patient trends and predicting outbreaks. Additionally, energy providers rely on it for consumption forecasting, and logistics companies use it to optimize routes and delivery schedules.

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Key Challenges in Real-World Deployment

Simple Models are not enough

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Comparison with other Foundational Models

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Visual Comparison With Other Foundational Models

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+ Benchmark Comparison +
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Scalability with Model Size

LPTM is also much smaller at around 150 million parameters compared to other solutions that range from 500 million to 2 billion parameters. This allows LPTM to deployed at a wider range of low resource computing platforms and can be deployed at a in settings with requirements of lowers latency and high throughput applications such as finance and sales forecasting.

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Next Steps

  • Find Applications for our Foundational Time-Series Analysis Models
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    Visualize Model Results

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    + TimesFM Visualization + LPTM Visualization + Chronos Visualization + Moment Imputation Visualization + Moment Forecasting Visualization + Moment Anomaly Detection Visualization +
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