time-series-forecasting

google Nov 4, 2025

Forecasting the future of forests with AI: From counting losses to predicting risk (opens in new tab)

Research from Google DeepMind and Google Research introduces ForestCast, a deep learning-based framework designed to transition forest management from retrospective loss monitoring to proactive risk forecasting. By utilizing vision transformers and pure satellite data, the team has developed a scalable method to predict future deforestation that matches or exceeds the accuracy of traditional models dependent on inconsistent manual inputs. This approach provides a repeatable, future-proof benchmark for protecting biodiversity and mitigating climate change on a global scale. ### Limitations of Traditional Forecasting * Existing state-of-the-art models rely on specialized geospatial maps, such as infrastructure development, road networks, and regional economic indicators. * These traditional inputs are often "patchy" and inconsistent across different countries, requiring manual assembly that is difficult to replicate globally. * Manual data sources are not future-proof; they tend to go out of date quickly with no guarantee of regular updates, unlike continuous satellite streams. ### A Scalable Pure-Satellite Architecture * The ForestCast model adopts a "pure satellite" approach, using only raw inputs from Landsat and Sentinel-2 satellites. * The architecture is built on vision transformers (ViTs) that process an entire tile of pixels in a single pass to capture critical spatial context and landscape-level trends. * The model incorporates a satellite-derived "change history" layer, which identifies previously deforested pixels and the specific year the loss occurred. * By avoiding socio-political or infrastructure maps, the method can be applied consistently to any region on Earth, allowing for meaningful cross-regional comparisons. ### Key Findings and Benchmark Release * Research indicates that "change history" is the most information-dense input; a model trained on this data alone performs almost as well as those using raw multi-spectral data. * The model successfully predicts tile-to-tile variation in deforestation amounts and identifies the specific pixels most likely to be cleared next. * Google has released the training and evaluation data as a public benchmark dataset, focusing initially on Southeast Asia to allow the machine learning community to verify and improve upon the results. The release of ForestCast provides a template for scaling predictive modeling to Latin America, Africa, and boreal latitudes. Conservationists and policymakers should utilize these forecasting tools to move beyond counting historical losses and instead direct resources toward "frontline" areas where the model identifies imminent risk of habitat conversion.

time-series-forecasting ai machine-learning deep-learning+4

google Sep 22, 2025

Time series foundation models can be few-shot learners (opens in new tab)

Researchers at Google have introduced TimesFM-ICF, a foundation model that enables time-series forecasting to transition from zero-shot to few-shot learning via in-context fine-tuning. By utilizing continued pre-training and specialized separator tokens, the model learns to adapt to a handful of related examples at inference time without requiring the complex supervised fine-tuning typically needed for task-specific optimization. This approach effectively matches or exceeds the performance of specialized models while maintaining the flexibility of a general-purpose foundation model. ### Overcoming the Limitations of Zero-Shot Models * Traditional forecasting often requires building separate, specialized models for every unique task, which is resource-intensive and slow. * While zero-shot models like the original TimesFM provide immediate forecasts without task-specific training, they cannot incorporate relevant context, such as data from nearby sensors or similar historical patterns. * The In-Context Fine-tuning (ICF) approach allows the model to "learn" from a few examples provided at the time of prediction, similar to how Large Language Models (LLMs) use few-shot prompting. ### Architecture and the Common Separator Token * TimesFM-ICF utilizes a patched decoder architecture that tokenizes 32 contiguous timepoints into a single input token. * To prevent the model from conflating different data streams—such as separate store locations or distinct time periods—researchers introduced a "common separator token" as a digital boundary between examples. * The model processes these tokens through a transformer stack using causal self-attention (CSA), ensuring it learns from historical context without accidentally "peeking" into the future. * A shared multilayer perceptron (MLP) translates the processed output tokens back into a forecast spanning 128 timepoints. ### Performance Benchmarking and Results * The model was evaluated on 23 unseen datasets, using the Mean Absolute Scaled Error (MASE) metric to aggregate performance across diverse time-series tasks. * TimesFM-ICF demonstrated a significant performance boost over the original zero-shot TimesFM and other state-of-the-art foundation models like Moirai and Lag-Llama. * Test results showed that providing just a few in-context examples allowed the model to match the accuracy of supervised fine-tuning, which normally requires much more computational overhead and data curation. TimesFM-ICF represents a practical shift for businesses managing diverse data streams, offering a way to achieve high-accuracy forecasts by simply providing a few relevant historical examples. For those looking to optimize inventory or energy demands, this method provides the precision of a custom-tuned model with the deployment speed of a pre-trained foundation model.

time-series-forecasting ai transformer foundation-models+3