Bird migration forecast maps

BirdCast, from Colorado State University and the Cornell Lab of Ornithology, shows current forecasts for where birds are headed over the United States:

Bird migration forecasts show predicted nocturnal migration 3 hours after local sunset and are updated every 6 hours. These forecasts come from models trained on the last 23 years of bird movements in the atmosphere as detected by the US NEXRAD weather surveillance radar network. In these models we use the Global Forecasting System (GFS) to predict suitable conditions for migration occurring three hours after local sunset.

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Forecasting Covid-19 cases in the early goings

There was a lot of uncertainty in the beginning of the pandemic, so the forecasts varied across sources. There were also many forecasts. Youyang Gu provided on of those forecasts, and it predicted well. Ashlee Vance reporting for Bloomberg on the Covid-19 forecasting work of Youyang Gu:

The novel, sophisticated twist of Gu’s model came from his use of machine learning algorithms to hone his figures. After MIT, Gu spent a couple years working in the financial industry writing algorithms for high-frequency trading systems in which his forecasts had to be accurate if he wanted to keep his job. When it came to Covid, Gu kept comparing his predictions to the eventual reported death totals and constantly tuned his machine learning software so that it would lead to ever more precise prognostications. Even though the work required the same hours as a demanding full-time job, Gu volunteered his time and lived off his savings. He wanted his data to be seen as free of any conflicts of interest or political bias.

Reading this, it felt a little bit like cherry-picking the forecast that was best, but I don’t know enough to decide. It does seem to highlight though some of the limitations of larger organizations that don’t always have the best point of view.

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FiveThirtyEight evaluates their forecasts

FiveThirtyEight uses forecasts to attach probabilities to politics and sports, and they get most of their attention before the events. After all, we don’t need a forecast after something happened. But forecasts aren’t useful if they don’t represent reality. So, FiveThirtyEight evaluated all of their projections.

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Prophet for forecasting with a lot of data

Facebook released Prophet, which is a procedure to quickly forecast with time series data.

Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.

Plus it’s available in both Python and R. What. Should be worth a look.

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