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. 2025 Oct 31;15(21):3180.
doi: 10.3390/ani15213180.

KINLI: Time Series Forecasting for Monitoring Poultry Health in Complex Pen Environments

Christopher Ingo Pack  1 , Tim Zeiser  2   3 , Christian Beecks  1   4 , Theo Lutz  2   3
Affiliations

KINLI: Time Series Forecasting for Monitoring Poultry Health in Complex Pen Environments

Christopher Ingo Pack et al. Animals (Basel). .

Abstract

We analyze how to perform accurate time series forecasting for monitoring poultry health in a complex pen environment. To this end, we make use of a novel dataset consisting of a collection of real-world sensor data in the housing of turkeys. The dataset comprises features such as food intake, water intake, and various environmental values, which come with high variance, sensor defects, and unreliable timestamps. In this paper, we investigate different state-of-the-art forecasting algorithms to predict different features, as well as a variety of deep learning models such as different transformer models and time series foundational models. We evaluate both their forecasting accuracy as well as the efforts required to run the models in the first place. Our findings show that some of these aforementioned algorithms are able to produce satisfactory forecasting results on this highly challenging dataset while still remaining easy to use, which is key in a tech-distant industry such as poultry farming.

Keywords: animal farming; deep learning; large language models; time series; time series forecasting; transformers.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Visualization of the KINLI dataset.
Figure 2
Figure 2
Forecast MAE ordered by forecasting length, with 12 corresponding to the 06:00:00 to 08:00:00 test, 16 being the 22:00:00 to eod test, 35 18:00:00, 72 12:00:00, and 94 08:00:00.
Figure 3
Figure 3
Forecasts of informer: water per day in ml (left), food per day in gr (center), water per food (right).
Figure 4
Figure 4
Forecasts of LLMs: Water per day in ml (left), Food per day in gr (center), Water per Food (right).
Figure 5
Figure 5
Forecasts of TimesFM on water per day in ml: 18:00:00 (left), 12:00:00 (center), 08:00:00 (right).
Figure 6
Figure 6
TimesFM long sequence forecasts for Water/Day in ml for 08:00:00 to 24:00:00 are able to better identify the shape of the dataset, but still fail to improve accuracy.
Figure 7
Figure 7
LLM long sequence forecasts for Water/Day in ml for 08:00:00 to 24:00:00 still show pattern deterioration.
Figure 8
Figure 8
Example of unrelated output with input (top) and output (bottom).
Figure 9
Figure 9
Examples of output deterioration with input (top) and precision deterioration (left) and pattern deterioration (right).
See this image and copyright information in PMC

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