Time Series Forecasting

netflixJun 19, 20263 min readCurated summary

Predicting Risk in Content Launches: How Data-Driven Insights can Transform Launch Planning

Netflix developed boosted-tree models to predict when in-progress productions will deliver Locked Cuts and final IMF media. The models address gaps and inaccuracies in manually maintained schedules, improving delivery-date accuracy and providing earlier warnings of risk. Backtesting shows that predictive dates reduce error and Accumulated Error Days (AED), a measure strongly associated with launch delays. ## Launch Preparation and Schedule Risk - After production, titles move through post-production and launch preparation. - Final IMF assets trigger work on: - Artwork and trailers - Subtitles - Maturity ratings - Quality control - Teams can begin earlier with a non-final Locked Cut, but later changes may require conformance work. - Waiting for the IMF risks compressing the launch timeline if delivery is late. ## Problems with Manual Schedules - Delivery estimates are manually supplied by content partners. - Schedules often contain missing dates and inaccurate estimates. - Dynamic production conditions—schedule changes, conflicts, and unforeseen obstacles—frequently cause delays. - Predictive modeling can fill missing ETAs and improve existing ones. ## Accumulated Error Days and Launch Misses - Accumulated Error Days (AED) measures the cumulative difference between estimated and actual delivery dates. - Titles with launch misses have significantly higher mean AED than titles without misses. - Inaccuracies close to delivery are more strongly associated with launch misses than errors accumulated over longer periods. - Improving schedule accuracy near launch is therefore especially valuable. ## Predicting Time to Delivery - Netflix uses boosted-tree regression models to predict the number of days until Locked Cut or IMF delivery. - Models use: - Production progress signals - Title metadata - Seasonal indicators - Daily snapshots of production data - Snapshot-based modeling keeps predictions current and supports changing features throughout all production phases. ## Evaluating Predictive Performance - Netflix compares predicted and scheduled dates using: - Mean and median absolute error - Mean and median bias - Error standard deviation - Rates of large errors beyond specified day thresholds - Predictive dates offer full coverage, unlike schedules that may lack estimates at some horizons. - Backtesting showed lower errors and fewer outliers for predicted IMF and Locked Cut dates. ## Earlier Accuracy Signals - Predictions can become reliable earlier than manual schedules. - Six months before Locked Cut delivery, predictions were more accurate than scheduled dates for 76% of titles. - Their 6.1-week mean absolute error matched the accuracy of scheduled dates only 11 weeks later. - Across six months before delivery, predicted dates reduced AED for most buying organizations and content types. ## Integrating Predictions into Existing Workflows - Because delivery dates already support stakeholder workflows, predictive estimates can be introduced without redesigning those processes. - The remaining challenge is deciding when to trust scheduled dates versus predictions. - Although predictions are generally more accurate, manual schedules can still outperform them in some situations, requiring a way to select the more reliable estimate. Netflix’s modeling approach turns production data into an ongoing risk signal rather than relying solely on static partner schedules. Using predictive dates alongside existing workflows can give teams earlier, more accurate information for launch planning and help reduce avoidable launch delays.

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Time Series Forecasting Machine Learning Predictive Modeling Analytics Engineering+3

googleMay 19, 20262 min readCurated summary

Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery

Empirical Research Assistance (ERA) is a Google AI system designed to help scientists develop expert-level computational models. Using Gemini, it searches literature, generates and evaluates code, and explores thousands of possible solutions through tree search. A Nature paper reports strong performance across scientific benchmarks, while new applications suggest ERA can accelerate research in health, climate, energy, and economics. ## How ERA Supports Scientific Coding - ERA starts with a scientific problem and a success metric. - It searches relevant research, combines methods, writes code, and iteratively tests and improves solutions. - Its tree-search process evaluates thousands of alternatives to optimize the resulting model. - Benchmarks in genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics showed expert-level performance. ## Applications to Open Scientific Problems - **Epidemiological forecasting** - Predicted U.S. hospital admissions up to four weeks ahead for flu, COVID-19, and RSV. - Forecasts ranked at or near the top of CDC leaderboards. - The techniques can potentially be adapted to other countries and diseases. - **California water-supply forecasting** - Produced seasonal runoff predictions for snow-fed river basins. - Delivered more accurate early forecasts than California’s official Bulletin 120 outlook. - Improved predictions could support water management and agriculture. - **Atmospheric carbon dioxide monitoring** - Combined geostationary weather-satellite data with other inputs to estimate CO₂ concentrations every 10 minutes across broad areas. - Captured urban emissions, plant-driven daytime absorption, and other atmospheric cycles. - Provides higher spatial and temporal coverage than measurements from satellites such as Orbiting Carbon Observatory-2. - **Solar-energy design** - Combined ERA with Google Antigravity to optimize three-dimensional solar-panel geometries. - Identified a 500-triangle volumetric fan design that could capture scattered radiation without backward shading. - **Retail forecasting** - Used economic indicators, Google Trends, historical patterns, and consumer sentiment. - Matched or exceeded commercial consensus forecasts and the Chicago Fed’s monthly retail forecast. ## Computational Discovery - Google is gradually opening access to Computational Discovery through a trusted tester program in Google Labs. - The system combines ERA with AlphaEvolve to support computational scientific investigation. - It complements other Gemini for Science experiments: - **Hypothesis Generation**, built with AI Co-Scientist, supports developing scientific hypotheses. - **Literature Insights** supports research and literature analysis. ERA’s demonstrated value lies in automating the labor-intensive cycle of designing, testing, and refining scientific software. Its expanding applications indicate that AI-assisted computational research could broaden access to advanced modeling while helping experts investigate complex scientific problems more quickly.

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Time Series Forecasting AI Machine Learning Gemini+4

googleNov 4, 2025Original article

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

googleSep 22, 2025Original article

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