Draft page to collect my notes about a time-series forecasting system. A lot of overlap with Microprediction.

# Scoring and rewards

If there are multiple participants, there needs to be a reward/punishment metric to transfer scores from bad predictors to good ones. This aspect might need some tweaking to get right.

Function to take a list of predictions and wager amounts, the correct result, and then produces new amounts where the wagers that were closer to the correct result earn a percentage from the wagers that were further away. Also the sum of the wagers should stay constant (zero-sum).

- Calculate the distance of each prediction from the correct result.
- Determine the total amount wagered.
- Calculate the proportion of each wager to the total wagered amount.
- Distribute the wagers from those further away to those closer to the correct result based on their distance and proportion.
- Ensure that the total amount of wagers remains constant.

```
def redistribute_wagers(predictions, wagers, correct_result):
# Calculate the distance of each prediction from the correct result
distances = [abs(prediction - correct_result) for prediction in predictions]
# Calculate the total amount wagered
total_wagered = sum(wagers)
# Calculate the proportion of each wager to the total wagered amount
proportions = [wager / total_wagered for wager in wagers]
# Calculate the total distance to distribute the wagers proportionally
total_distance = sum(distances)
# Calculate the amount to be redistributed to each wager based on distance
redistribution = [((total_distance - distance) / total_distance) * total_wagered for distance in distances]
# Adjust the redistribution amounts to ensure zero-sum
redistribution_adjusted = [redistribution[i] - (proportions[i] * sum(redistribution)) for i in range(len(wagers))]
# Calculate the new wager amounts
new_wagers = [wagers[i] + redistribution_adjusted[i] for i in range(len(wagers))]
return new_wagers
# Example usage:
predictions = [3, 5, 7]
wagers = [100, 150, 200]
correct_result = 4
new_wagers = redistribute_wagers(predictions, wagers, correct_result)
print(new_wagers)
```

This feels similar to a softmax. I should investigate that.

# Workflow

- How to collect/update/cancel and score predictions?

# Data source ideas

- Dweet.io has a nice variety of sensor data.
- https://dweet.io/get/stats -> List of recently active data sources.
- Get them, push to ClickHouse.

- Weather, obviously. It’s very easily available, and has such a large impact on other useful things like energy use and the general mood of the population.
- METAR reports
- https://aviationweather.gov/data/api/

- Ezoic Ad Revenue Index
- Exchange rates

# Microprediction

- https://web.archive.org/web/20230306165735/http://api.microprediction.org/
- http://web.archive.org/web/20201129125905/http://config.microprediction.org/config.json

## Delays

- 70 seconds
- 310 seconds
- 910 seconds
- 3555 seconds

225 points are sent as a prediction. Think of these as samples from the probability distribution that you predict.