Abstract: Selecting a timeseries data set by determining a data quality score and seasonality period for each segment of a set of timeseries data segments, determining a most frequent seasonality period for the set of timeseries data segments, determining an average data quality score for a set of timeseries data segments having the most frequent seasonality period, forming a timeseries data set from the set of segments having the most frequent seasonality period, according to a desired data quality score, and providing the timeseries data set for training a machine learning model.

Abstract: In an approach for automatically updating the preprocessing of time series data for better AI, a processor identifies a set of characteristics from historic sensor data of a sensor, wherein the set of characteristics includes an original data granularity. A processor applies preprocessing to incoming sensor data of the sensor based on the set of characteristics. A processor, responsive to a pre-defined period of time passing, determines that a data granularity of the incoming sensor data has changed. A processor determines a new data granularity of the incoming sensor data. A processor updates the preprocessing of the incoming sensor data based on the new data granularity.

Abstract: Aggregation of traffic impact metrics is provided. Each of a plurality of holidays is associated with a holiday category of a plurality of holiday categories. The plurality of holiday categories includes a first holiday category and a second holiday category. A plurality of points of interest along a link of a transportation network is identified. At least one of the plurality of points of interest is associated with the first holiday category and at least one of the plurality of points of interest with the second holiday category. A mean category impact for each of the plurality of holiday categories is determined. An aggregated traffic impact metric is determined based, at least in part, on the mean category impact of each of the plurality of holiday categories.

Abstract: Aggregation of traffic impact metrics is provided. Each of a plurality of holidays is associated with a holiday category of a plurality of holiday categories. The plurality of holiday categories includes a first holiday category and a second holiday category. A plurality of points of interest along a link of a transportation network is identified. At least one of the plurality of points of interest is associated with the first holiday category and at least one of the plurality of points of interest with the second holiday category. A mean category impact for each of the plurality of holiday categories is determined. An aggregated traffic impact metric is determined based, at least in part, on the mean category impact of each of the plurality of holiday categories.

Abstract: A method for auto-calibrating parameters in traffic prediction. The method includes determining a first subnet of traffic links that is associated with a plurality of traffic links in a traffic network. The method includes determining a second subnet of traffic links that is associated with the first subnet of traffic links and has a first traffic predicting accuracy value. The method includes generating a set of optimized traffic predicting parameters associated with the second subnet of traffic links, and applying the set of optimized traffic parameters onto a third subnet of traffic links. The method includes determining the set of optimized traffic predicting parameters used to calculate prediction results having a second traffic predicting accuracy value, and applying said set of optimized traffic predicting parameters to subnets associated with the traffic network. Further, the first traffic predicting accuracy value is lower than the second traffic predicting accuracy value.

Abstract: A method for auto-calibrating parameters in traffic prediction. The method includes determining a first subnet of traffic links that is associated with a plurality of traffic links in a traffic network. The method includes determining a second subnet of traffic links that is associated with the first subnet of traffic links and has a first traffic predicting accuracy value. The method includes generating a set of optimized traffic predicting parameters associated with the second subnet of traffic links, and applying the set of optimized traffic parameters onto a third subnet of traffic links. The method includes determining the set of optimized traffic predicting parameters used to calculate prediction results having a second traffic predicting accuracy value, and applying said set of optimized traffic predicting parameters to subnets associated with the traffic network. Further, the first traffic predicting accuracy value is lower than the second traffic predicting accuracy value.

Abstract: A method for auto-calibrating parameters in traffic prediction. The method includes determining a first subnet of traffic links that is associated with a plurality of traffic links in a traffic network. The method includes determining a second subnet of traffic links that is associated with the first subnet of traffic links and has a first traffic predicting accuracy value. The method includes generating a set of optimized traffic predicting parameters associated with the second subnet of traffic links, and applying the set of optimized traffic parameters onto a third subnet of traffic links. The method includes determining the set of optimized traffic predicting parameters used to calculate prediction results having a second traffic predicting accuracy value, and applying said set of optimized traffic predicting parameters to subnets associated with the traffic network. Further, the first traffic predicting accuracy value is lower than the second traffic predicting accuracy value.

Abstract: A method for auto-calibrating parameters in traffic prediction. The method includes determining a first subnet of traffic links that is associated with a plurality of traffic links in a traffic network. The method includes determining a second subnet of traffic links that is associated with the first subnet of traffic links and has a first traffic predicting accuracy value. The method includes generating a set of optimized traffic predicting parameters associated with the second subnet of traffic links, and applying the set of optimized traffic parameters onto a third subnet of traffic links. The method includes determining the set of optimized traffic predicting parameters used to calculate prediction results having a second traffic predicting accuracy value, and applying said set of optimized traffic predicting parameters to subnets associated with the traffic network. Further, the first traffic predicting accuracy value is lower than the second traffic predicting accuracy value.