Understanding our Prediciton Series

Prediction Series are used to catalogue the different predictions we generate, where as the Predictions themselves are generated as Prediction Events.

Properties of a Prediction Series

Prediction Series are categorised using the following properties:

Property	Description
product	The different products in the market the series relates to, such as mFRR EAM Up or Imbalance
area	The geographical region, such as NO1 or FI
statistic	Defines something about the shape of the data in each prediction event. More on the different statistics below
unit_type	What it is that is being predicted, More on the different unit types below
unit	EUR, MW or N/A for series that are probabilities (unitless)
resolution	The period for which each event is valid for. More on this when we look at the Prediction Events

Unit Type

Price

The expected price.

Price Spread

The expected difference (spread) from the Spot price.

Direction

Which direction will be dominant.

Volume

The expected volume.

Statistics

Point

A single expected value for the corresponding Product and Unit Type.

Quantile

Probabilities the Product being predicted will fall into a certain range. For example, given the product Imbalance, and unit type of Price_Spread:

{
    "10": -9.918647732527685,
    "25": -5.9330300407128735,
    "50": -2.14651955171376,
    "75": 1.8668928923393722,
    "90": 8.326481400150248
}

• There is a 10% probability that the price will be 9.91€ below the spot price
• There is a 25% probability that the price will be 5.93€ below the spot price
• The 50% quantile (also referred to as median), means there is a 50% probability the price will be above (Spot price - 2.14)€, and a 50% probability the price will be below (Spot price - 2.14)€
• There is a 25% probability that the price will be 1.86€ above the spot price
• There is a 10% probability that the price will be 8.32€ above the spot price

Distribution

The empirical distribution function, which is an estimate of the cumulative distribution function that generated the points in the sample. It converges with probability 1 to that underlying distribution. For example, given the product Imbalance , and unit type of Direction

{
    "up": 0.2971276214864301,
    "down": 0.36712538740813155,
    "none": 0.33574699110543826
}

• Thers is a 36% probability that the dominant direction will be down

Conditional Index

Probabilities for different thresholds the series will exceed. For example, given the product Imbalance, and unit type of Price_Spread:

{
    "20": 0.06071186939588584,
    "50": 0.03407819229710775,
    "100": 0.026504537656296383,
    "-20": 0.07577408096156756,
    "-50": 0.012431568356134725,
    "-100": 0.005805317597058654
}

• "20" refers to the the probability the price will be 20€ higher than spot (~6%)
• "-50" is the probability the price will be 50€ below spot (~1.2%).