SYSTEMS AND METHODS FOR TIME SERIES MODELING

1 . A system, comprising:

one or more processors, coupled to memory, to:

identify a first dataset comprising a plurality of time series having a plurality of characteristics, wherein a first time series of the plurality of time series comprises one or more characteristics of the plurality of characteristics that are different from characteristics of a second time series of the plurality of time series;

select, based at least in part on the plurality of characteristics, a plurality of models;

train, via machine learning, the plurality of models with the first dataset;

generate a model based at least in part on a combination of the plurality of models; and

deploy the model to output one or more predictions responsive to a second dataset, different from the first dataset, having at least one of the plurality of characteristics.

2 . The system of claim 1 , wherein the one or more processors are further configured to:

determine that multiple rows in the first dataset comprise a same timestamp;

provide, responsive to the determination, a prompt via a graphical user interface displayed on a display device coupled to a computing device;

receive, via the prompt from the computing device, an indication that the first dataset comprises more than one time series; and

determine to select the plurality of models based at least in part on the indication received from the computing device.

3 . The system of claim 1 , wherein the one or more processors are further configured to:

provide, for display via a graphical user interface presented on a display device coupled to a computing device, a prompt to split the first dataset by segments;

receive, via the graphical user interface from the computing device, an indication to split the first dataset by segments; and

split, responsive to the indication, the first dataset into segments.

4 . The system of claim 1 , wherein the one or more processors are further configured to:

provide, via a graphical user interface presented by a display device of a computing device, a user interface element to adjust at least one of a first window used to derive one or more features from the first dataset or a second window over which to predict values for the one or more features.

5 . The system of claim 4 , wherein the one or more processors are further configured to:

provide, via the graphical user interface, an indication of a forecast point at or between the first window and the second window.

6 . The system of claim 5 , wherein the one or more processors are further configured to:

identify a blind history gap between the first window and the forecast point presented via the graphical user interface; and

provide an indication via the graphical user interface of the blind history gap.

7 . The system of claim 5 , wherein the one or more processors are further configured to:

identify, based at least on the forecast point and the second window, a gap for which the model is unable to make predictions.

8 . The system of claim 1 , wherein the one or more processors are further configured to:

provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to select a configuration for a backtest;

receive, via the user interface element, a selection of the configuration for the backtest; and

provide, for presentation by the graphical user interface, an indication of at least one of a validation portion for the backtest, a primary training data portion for the backtest, a gap for the backtest, or a holdout portion for the backtest.

9 . The system of claim 1 , wherein the one or more processors are further configured to:

provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to input a calendar of events to generate a feature for the plurality of time series;

receive, via the user interface element, the calendar of events; and

derive one or more features of the first dataset using the calendar of events.

10 . The system of claim 1 , wherein the plurality of characteristics comprise at least one of seasonality, frequency content, average target values, maximum target values, minimum target values, or a number of zero values.

11 . The system of claim 1 , wherein the one or more processors are further configured to:

map each time series in the plurality of time series to at least one model in the plurality of models to select the plurality of models.

12 . The system of claim 11 , wherein the one or more processors are further configured to:

cluster the time series in the plurality of time series into a plurality of groups, wherein each group in the plurality of groups comprises common or similar characteristics from the characteristics; and

assign each group to a respective model from the plurality of models to select the plurality of models.

13 . A method, comprising:

identifying, by one or more processors coupled to memory, a first dataset comprising a plurality of time series having a plurality of characteristics, wherein a first time series of the plurality of time series comprises one or more characteristics of the plurality of characteristics that are different from characteristics of a second time series of the plurality of time series;

selecting, by the one or more processors based at least in part on the plurality of characteristics, a plurality of models;

training, by the one or more processors via machine learning, the plurality of models with the first dataset;

generating, by the one or more processors, a model based at least in part on a combination of the plurality of models; and

deploying, by the one or more processors, the model to output one or more predictions responsive to a second dataset, different from the first dataset, having at least one of the plurality of characteristics.

14 . The method of claim 13 , comprising:

determining, by the one or more processors, that multiple rows in the first dataset comprise a same timestamp;

providing, by the one or more processors responsive to the determination, a prompt via a graphical user interface displayed on a display device coupled to a computing device;

receiving, by the one or more processors via the prompt from the computing device, an indication that the first dataset comprises more than one time series; and

determining, by the one or more processors, to select the plurality of models based at least in part on the indication received from the computing device.

15 . The method of claim 13 , comprising:

providing, by the one or more processors, for display via a graphical user interface presented on a display device coupled to a computing device, a prompt to split the first dataset by segments;

receiving, by the one or more processors via the graphical user interface from the computing device, an indication to split the first dataset by segments; and

splitting, by the one or more processors responsive to the indication, the first dataset into segments.

16 . The method of claim 13 , comprising:

providing, by the one or more processors via a graphical user interface presented by a display device of a computing device, a user interface element to adjust at least one of a first window used to derive one or more features from the first dataset or a second window over which to predict values for the one or more features.

17 . The method of claim 16 , comprising:

providing, by the one or more processors via the graphical user interface, an indication of a forecast point at or between the first window and the second window.

18 . The method of claim 13 , comprising:

providing, by the one or more processors, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to select a configuration for a backtest;

receiving, by the one or more processors via the user interface element, a selection of the configuration for the backtest; and

providing, by the one or more processors, for presentation by the graphical user interface, an indication of at least one of a validation portion for the backtest, a primary training data portion for the backtest, a gap for the backtest, or a holdout portion for the backtest.

19 . A non-transitory computer-readable medium storing processor executable instructions that, when executed by one or more processors, cause the one or more processors to:

identify a first dataset comprising a plurality of time series having a plurality of characteristics, wherein a first time series of the plurality of time series comprises one or more characteristics of the plurality of characteristics that are different from characteristics of a second time series of the plurality of time series;

select, based at least in part on the plurality of characteristics, a plurality of models;

train, via machine learning, the plurality of models with the first dataset;

generate a model based at least in part on a combination of the plurality of models; and

deploy the model to output one or more predictions responsive to a second dataset, different from the first dataset, having at least one of the plurality of characteristics.

20 . The computer-readable medium of claim 19 , wherein the instructions further comprise instructions to:

determine that multiple rows in the first dataset comprise a same timestamp;

provide, responsive to the determination, a prompt via a graphical user interface displayed on a display device coupled to a computing device;

receive, via the prompt from the computing device, an indication that the first dataset comprises more than one time series; and

determine to select the plurality of models based at least in part on the indication received from the computing device.