1// Code generated by smithy-go-codegen DO NOT EDIT. 2 3package types 4 5import ( 6 "time" 7) 8 9// Specifies a categorical hyperparameter and it's range of tunable values. This 10// object is part of the ParameterRanges object. 11type CategoricalParameterRange struct { 12 13 // The name of the categorical hyperparameter to tune. 14 // 15 // This member is required. 16 Name *string 17 18 // A list of the tunable categories for the hyperparameter. 19 // 20 // This member is required. 21 Values []string 22} 23 24// Specifies a continuous hyperparameter and it's range of tunable values. This 25// object is part of the ParameterRanges object. 26type ContinuousParameterRange struct { 27 28 // The maximum tunable value of the hyperparameter. 29 // 30 // This member is required. 31 MaxValue *float64 32 33 // The minimum tunable value of the hyperparameter. 34 // 35 // This member is required. 36 MinValue *float64 37 38 // The name of the hyperparameter to tune. 39 // 40 // This member is required. 41 Name *string 42 43 // The scale that hyperparameter tuning uses to search the hyperparameter range. 44 // Valid values: Auto Amazon Forecast hyperparameter tuning chooses the best scale 45 // for the hyperparameter. Linear Hyperparameter tuning searches the values in the 46 // hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning 47 // searches the values in the hyperparameter range by using a logarithmic scale. 48 // Logarithmic scaling works only for ranges that have values greater than 0. 49 // ReverseLogarithmic hyperparameter tuning searches the values in the 50 // hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic 51 // scaling works only for ranges that are entirely within the range 0 <= x < 1.0. 52 // For information about choosing a hyperparameter scale, see Hyperparameter 53 // Scaling 54 // (http://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). 55 // One of the following values: 56 ScalingType ScalingType 57} 58 59// The destination for an export job. Provide an S3 path, an AWS Identity and 60// Access Management (IAM) role that allows Amazon Forecast to access the location, 61// and an AWS Key Management Service (KMS) key (optional). 62type DataDestination struct { 63 64 // The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the 65 // credentials to access the bucket. 66 // 67 // This member is required. 68 S3Config *S3Config 69} 70 71// Provides a summary of the dataset group properties used in the ListDatasetGroups 72// operation. To get the complete set of properties, call the DescribeDatasetGroup 73// operation, and provide the DatasetGroupArn. 74type DatasetGroupSummary struct { 75 76 // When the dataset group was created. 77 CreationTime *time.Time 78 79 // The Amazon Resource Name (ARN) of the dataset group. 80 DatasetGroupArn *string 81 82 // The name of the dataset group. 83 DatasetGroupName *string 84 85 // When the dataset group was created or last updated from a call to the 86 // UpdateDatasetGroup operation. While the dataset group is being updated, 87 // LastModificationTime is the current time of the ListDatasetGroups call. 88 LastModificationTime *time.Time 89} 90 91// Provides a summary of the dataset import job properties used in the 92// ListDatasetImportJobs operation. To get the complete set of properties, call the 93// DescribeDatasetImportJob operation, and provide the DatasetImportJobArn. 94type DatasetImportJobSummary struct { 95 96 // When the dataset import job was created. 97 CreationTime *time.Time 98 99 // The location of the training data to import and an AWS Identity and Access 100 // Management (IAM) role that Amazon Forecast can assume to access the data. The 101 // training data must be stored in an Amazon S3 bucket. If encryption is used, 102 // DataSource includes an AWS Key Management Service (KMS) key. 103 DataSource *DataSource 104 105 // The Amazon Resource Name (ARN) of the dataset import job. 106 DatasetImportJobArn *string 107 108 // The name of the dataset import job. 109 DatasetImportJobName *string 110 111 // The last time the resource was modified. The timestamp depends on the status of 112 // the job: 113 // 114 // * CREATE_PENDING - The CreationTime. 115 // 116 // * CREATE_IN_PROGRESS - The 117 // current timestamp. 118 // 119 // * CREATE_STOPPING - The current timestamp. 120 // 121 // * CREATE_STOPPED 122 // - When the job stopped. 123 // 124 // * ACTIVE or CREATE_FAILED - When the job finished or 125 // failed. 126 LastModificationTime *time.Time 127 128 // If an error occurred, an informational message about the error. 129 Message *string 130 131 // The status of the dataset import job. States include: 132 // 133 // * ACTIVE 134 // 135 // * 136 // CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED 137 // 138 // * DELETE_PENDING, 139 // DELETE_IN_PROGRESS, DELETE_FAILED 140 // 141 // * CREATE_STOPPING, CREATE_STOPPED 142 Status *string 143} 144 145// Provides a summary of the dataset properties used in the ListDatasets operation. 146// To get the complete set of properties, call the DescribeDataset operation, and 147// provide the DatasetArn. 148type DatasetSummary struct { 149 150 // When the dataset was created. 151 CreationTime *time.Time 152 153 // The Amazon Resource Name (ARN) of the dataset. 154 DatasetArn *string 155 156 // The name of the dataset. 157 DatasetName *string 158 159 // The dataset type. 160 DatasetType DatasetType 161 162 // The domain associated with the dataset. 163 Domain Domain 164 165 // When you create a dataset, LastModificationTime is the same as CreationTime. 166 // While data is being imported to the dataset, LastModificationTime is the current 167 // time of the ListDatasets call. After a CreateDatasetImportJob operation has 168 // finished, LastModificationTime is when the import job completed or failed. 169 LastModificationTime *time.Time 170} 171 172// The source of your training data, an AWS Identity and Access Management (IAM) 173// role that allows Amazon Forecast to access the data and, optionally, an AWS Key 174// Management Service (KMS) key. This object is submitted in the 175// CreateDatasetImportJob request. 176type DataSource struct { 177 178 // The path to the training data stored in an Amazon Simple Storage Service (Amazon 179 // S3) bucket along with the credentials to access the data. 180 // 181 // This member is required. 182 S3Config *S3Config 183} 184 185// An AWS Key Management Service (KMS) key and an AWS Identity and Access 186// Management (IAM) role that Amazon Forecast can assume to access the key. You can 187// specify this optional object in the CreateDataset and CreatePredictor requests. 188type EncryptionConfig struct { 189 190 // The Amazon Resource Name (ARN) of the KMS key. 191 // 192 // This member is required. 193 KMSKeyArn *string 194 195 // The ARN of the IAM role that Amazon Forecast can assume to access the AWS KMS 196 // key. Passing a role across AWS accounts is not allowed. If you pass a role that 197 // isn't in your account, you get an InvalidInputException error. 198 // 199 // This member is required. 200 RoleArn *string 201} 202 203// Provides detailed error metrics to evaluate the performance of a predictor. This 204// object is part of the Metrics object. 205type ErrorMetric struct { 206 207 // The Forecast type used to compute WAPE and RMSE. 208 ForecastType *string 209 210 // The root-mean-square error (RMSE). 211 RMSE *float64 212 213 // The weighted absolute percentage error (WAPE). 214 WAPE *float64 215} 216 217// Parameters that define how to split a dataset into training data and testing 218// data, and the number of iterations to perform. These parameters are specified in 219// the predefined algorithms but you can override them in the CreatePredictor 220// request. 221type EvaluationParameters struct { 222 223 // The point from the end of the dataset where you want to split the data for model 224 // training and testing (evaluation). Specify the value as the number of data 225 // points. The default is the value of the forecast horizon. BackTestWindowOffset 226 // can be used to mimic a past virtual forecast start date. This value must be 227 // greater than or equal to the forecast horizon and less than half of the 228 // TARGET_TIME_SERIES dataset length. ForecastHorizon <= BackTestWindowOffset < 1/2 229 // * TARGET_TIME_SERIES dataset length 230 BackTestWindowOffset *int32 231 232 // The number of times to split the input data. The default is 1. Valid values are 233 // 1 through 5. 234 NumberOfBacktestWindows *int32 235} 236 237// The results of evaluating an algorithm. Returned as part of the 238// GetAccuracyMetrics response. 239type EvaluationResult struct { 240 241 // The Amazon Resource Name (ARN) of the algorithm that was evaluated. 242 AlgorithmArn *string 243 244 // The array of test windows used for evaluating the algorithm. The 245 // NumberOfBacktestWindows from the EvaluationParameters object determines the 246 // number of windows in the array. 247 TestWindows []WindowSummary 248} 249 250// Provides featurization (transformation) information for a dataset field. This 251// object is part of the FeaturizationConfig object. For example: { 252// 253// "AttributeName": "demand", 254// 255// FeaturizationPipeline [ { 256// 257// 258// "FeaturizationMethodName": "filling", 259// 260// "FeaturizationMethodParameters": 261// {"aggregation": "avg", "backfill": "nan"} 262// 263// } ] 264// 265// } 266type Featurization struct { 267 268 // The name of the schema attribute that specifies the data field to be featurized. 269 // Amazon Forecast supports the target field of the TARGET_TIME_SERIES and the 270 // RELATED_TIME_SERIES datasets. For example, for the RETAIL domain, the target is 271 // demand, and for the CUSTOM domain, the target is target_value. For more 272 // information, see howitworks-missing-values. 273 // 274 // This member is required. 275 AttributeName *string 276 277 // An array of one FeaturizationMethod object that specifies the feature 278 // transformation method. 279 FeaturizationPipeline []FeaturizationMethod 280} 281 282// In a CreatePredictor operation, the specified algorithm trains a model using the 283// specified dataset group. You can optionally tell the operation to modify data 284// fields prior to training a model. These modifications are referred to as 285// featurization. You define featurization using the FeaturizationConfig object. 286// You specify an array of transformations, one for each field that you want to 287// featurize. You then include the FeaturizationConfig object in your 288// CreatePredictor request. Amazon Forecast applies the featurization to the 289// TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training. You 290// can create multiple featurization configurations. For example, you might call 291// the CreatePredictor operation twice by specifying different featurization 292// configurations. 293type FeaturizationConfig struct { 294 295 // The frequency of predictions in a forecast. Valid intervals are Y (Year), M 296 // (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 297 // 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" 298 // indicates every year and "5min" indicates every five minutes. The frequency must 299 // be greater than or equal to the TARGET_TIME_SERIES dataset frequency. When a 300 // RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the 301 // RELATED_TIME_SERIES dataset frequency. 302 // 303 // This member is required. 304 ForecastFrequency *string 305 306 // An array of featurization (transformation) information for the fields of a 307 // dataset. 308 Featurizations []Featurization 309 310 // An array of dimension (field) names that specify how to group the generated 311 // forecast. For example, suppose that you are generating a forecast for item sales 312 // across all of your stores, and your dataset contains a store_id field. If you 313 // want the sales forecast for each item by store, you would specify store_id as 314 // the dimension. All forecast dimensions specified in the TARGET_TIME_SERIES 315 // dataset don't need to be specified in the CreatePredictor request. All forecast 316 // dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the 317 // CreatePredictor request. 318 ForecastDimensions []string 319} 320 321// Provides information about the method that featurizes (transforms) a dataset 322// field. The method is part of the FeaturizationPipeline of the Featurization 323// object. The following is an example of how you specify a FeaturizationMethod 324// object. { 325// "FeaturizationMethodName": "filling", 326// 327// 328// "FeaturizationMethodParameters": {"aggregation": "sum", "middlefill": "zero", 329// "backfill": "zero"} 330// 331// } 332type FeaturizationMethod struct { 333 334 // The name of the method. The "filling" method is the only supported method. 335 // 336 // This member is required. 337 FeaturizationMethodName FeaturizationMethodName 338 339 // The method parameters (key-value pairs), which are a map of override parameters. 340 // Specify these parameters to override the default values. Related Time Series 341 // attributes do not accept aggregation parameters. The following list shows the 342 // parameters and their valid values for the "filling" featurization method for a 343 // Target Time Series dataset. Bold signifies the default value. 344 // 345 // * aggregation: 346 // sum, avg, first, min, max 347 // 348 // * frontfill: none 349 // 350 // * middlefill: zero, nan (not a 351 // number), value, median, mean, min, max 352 // 353 // * backfill: zero, nan, value, median, 354 // mean, min, max 355 // 356 // The following list shows the parameters and their valid values 357 // for a Related Time Series featurization method (there are no defaults): 358 // 359 // * 360 // middlefill: zero, value, median, mean, min, max 361 // 362 // * backfill: zero, value, 363 // median, mean, min, max 364 // 365 // * futurefill: zero, value, median, mean, min, max 366 // 367 // To 368 // set a filling method to a specific value, set the fill parameter to value and 369 // define the value in a corresponding _value parameter. For example, to set 370 // backfilling to a value of 2, include the following: "backfill": "value" and 371 // "backfill_value":"2". 372 FeaturizationMethodParameters map[string]string 373} 374 375// Describes a filter for choosing a subset of objects. Each filter consists of a 376// condition and a match statement. The condition is either IS or IS_NOT, which 377// specifies whether to include or exclude the objects that match the statement, 378// respectively. The match statement consists of a key and a value. 379type Filter struct { 380 381 // The condition to apply. To include the objects that match the statement, specify 382 // IS. To exclude matching objects, specify IS_NOT. 383 // 384 // This member is required. 385 Condition FilterConditionString 386 387 // The name of the parameter to filter on. 388 // 389 // This member is required. 390 Key *string 391 392 // The value to match. 393 // 394 // This member is required. 395 Value *string 396} 397 398// Provides a summary of the forecast export job properties used in the 399// ListForecastExportJobs operation. To get the complete set of properties, call 400// the DescribeForecastExportJob operation, and provide the listed 401// ForecastExportJobArn. 402type ForecastExportJobSummary struct { 403 404 // When the forecast export job was created. 405 CreationTime *time.Time 406 407 // The path to the Amazon Simple Storage Service (Amazon S3) bucket where the 408 // forecast is exported. 409 Destination *DataDestination 410 411 // The Amazon Resource Name (ARN) of the forecast export job. 412 ForecastExportJobArn *string 413 414 // The name of the forecast export job. 415 ForecastExportJobName *string 416 417 // The last time the resource was modified. The timestamp depends on the status of 418 // the job: 419 // 420 // * CREATE_PENDING - The CreationTime. 421 // 422 // * CREATE_IN_PROGRESS - The 423 // current timestamp. 424 // 425 // * CREATE_STOPPING - The current timestamp. 426 // 427 // * CREATE_STOPPED 428 // - When the job stopped. 429 // 430 // * ACTIVE or CREATE_FAILED - When the job finished or 431 // failed. 432 LastModificationTime *time.Time 433 434 // If an error occurred, an informational message about the error. 435 Message *string 436 437 // The status of the forecast export job. States include: 438 // 439 // * ACTIVE 440 // 441 // * 442 // CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED 443 // 444 // * CREATE_STOPPING, 445 // CREATE_STOPPED 446 // 447 // * DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED 448 // 449 // The Status 450 // of the forecast export job must be ACTIVE before you can access the forecast in 451 // your S3 bucket. 452 Status *string 453} 454 455// Provides a summary of the forecast properties used in the ListForecasts 456// operation. To get the complete set of properties, call the DescribeForecast 457// operation, and provide the ForecastArn that is listed in the summary. 458type ForecastSummary struct { 459 460 // When the forecast creation task was created. 461 CreationTime *time.Time 462 463 // The Amazon Resource Name (ARN) of the dataset group that provided the data used 464 // to train the predictor. 465 DatasetGroupArn *string 466 467 // The ARN of the forecast. 468 ForecastArn *string 469 470 // The name of the forecast. 471 ForecastName *string 472 473 // The last time the resource was modified. The timestamp depends on the status of 474 // the job: 475 // 476 // * CREATE_PENDING - The CreationTime. 477 // 478 // * CREATE_IN_PROGRESS - The 479 // current timestamp. 480 // 481 // * CREATE_STOPPING - The current timestamp. 482 // 483 // * CREATE_STOPPED 484 // - When the job stopped. 485 // 486 // * ACTIVE or CREATE_FAILED - When the job finished or 487 // failed. 488 LastModificationTime *time.Time 489 490 // If an error occurred, an informational message about the error. 491 Message *string 492 493 // The ARN of the predictor used to generate the forecast. 494 PredictorArn *string 495 496 // The status of the forecast. States include: 497 // 498 // * ACTIVE 499 // 500 // * CREATE_PENDING, 501 // CREATE_IN_PROGRESS, CREATE_FAILED 502 // 503 // * CREATE_STOPPING, CREATE_STOPPED 504 // 505 // * 506 // DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED 507 // 508 // The Status of the forecast 509 // must be ACTIVE before you can query or export the forecast. 510 Status *string 511} 512 513// Configuration information for a hyperparameter tuning job. You specify this 514// object in the CreatePredictor request. A hyperparameter is a parameter that 515// governs the model training process. You set hyperparameters before training 516// starts, unlike model parameters, which are determined during training. The 517// values of the hyperparameters effect which values are chosen for the model 518// parameters. In a hyperparameter tuning job, Amazon Forecast chooses the set of 519// hyperparameter values that optimize a specified metric. Forecast accomplishes 520// this by running many training jobs over a range of hyperparameter values. The 521// optimum set of values depends on the algorithm, the training data, and the 522// specified metric objective. 523type HyperParameterTuningJobConfig struct { 524 525 // Specifies the ranges of valid values for the hyperparameters. 526 ParameterRanges *ParameterRanges 527} 528 529// The data used to train a predictor. The data includes a dataset group and any 530// supplementary features. You specify this object in the CreatePredictor request. 531type InputDataConfig struct { 532 533 // The Amazon Resource Name (ARN) of the dataset group. 534 // 535 // This member is required. 536 DatasetGroupArn *string 537 538 // An array of supplementary features. The only supported feature is a holiday 539 // calendar. 540 SupplementaryFeatures []SupplementaryFeature 541} 542 543// Specifies an integer hyperparameter and it's range of tunable values. This 544// object is part of the ParameterRanges object. 545type IntegerParameterRange struct { 546 547 // The maximum tunable value of the hyperparameter. 548 // 549 // This member is required. 550 MaxValue *int32 551 552 // The minimum tunable value of the hyperparameter. 553 // 554 // This member is required. 555 MinValue *int32 556 557 // The name of the hyperparameter to tune. 558 // 559 // This member is required. 560 Name *string 561 562 // The scale that hyperparameter tuning uses to search the hyperparameter range. 563 // Valid values: Auto Amazon Forecast hyperparameter tuning chooses the best scale 564 // for the hyperparameter. Linear Hyperparameter tuning searches the values in the 565 // hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning 566 // searches the values in the hyperparameter range by using a logarithmic scale. 567 // Logarithmic scaling works only for ranges that have values greater than 0. 568 // ReverseLogarithmic Not supported for IntegerParameterRange. Reverse logarithmic 569 // scaling works only for ranges that are entirely within the range 0 <= x < 1.0. 570 // For information about choosing a hyperparameter scale, see Hyperparameter 571 // Scaling 572 // (http://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). 573 // One of the following values: 574 ScalingType ScalingType 575} 576 577// Provides metrics that are used to evaluate the performance of a predictor. This 578// object is part of the WindowSummary object. 579type Metrics struct { 580 581 // Provides detailed error metrics on forecast type, root-mean square-error (RMSE), 582 // and weighted average percentage error (WAPE). 583 ErrorMetrics []ErrorMetric 584 585 // The root-mean-square error (RMSE). 586 // 587 // Deprecated: This property is deprecated, please refer to ErrorMetrics for both 588 // RMSE and WAPE 589 RMSE *float64 590 591 // An array of weighted quantile losses. Quantiles divide a probability 592 // distribution into regions of equal probability. The distribution in this case is 593 // the loss function. 594 WeightedQuantileLosses []WeightedQuantileLoss 595} 596 597// Specifies the categorical, continuous, and integer hyperparameters, and their 598// ranges of tunable values. The range of tunable values determines which values 599// that a hyperparameter tuning job can choose for the specified hyperparameter. 600// This object is part of the HyperParameterTuningJobConfig object. 601type ParameterRanges struct { 602 603 // Specifies the tunable range for each categorical hyperparameter. 604 CategoricalParameterRanges []CategoricalParameterRange 605 606 // Specifies the tunable range for each continuous hyperparameter. 607 ContinuousParameterRanges []ContinuousParameterRange 608 609 // Specifies the tunable range for each integer hyperparameter. 610 IntegerParameterRanges []IntegerParameterRange 611} 612 613// Provides a summary of the predictor backtest export job properties used in the 614// ListPredictorBacktestExportJobs operation. To get a complete set of properties, 615// call the DescribePredictorBacktestExportJob operation, and provide the listed 616// PredictorBacktestExportJobArn. 617type PredictorBacktestExportJobSummary struct { 618 619 // When the predictor backtest export job was created. 620 CreationTime *time.Time 621 622 // The destination for an export job. Provide an S3 path, an AWS Identity and 623 // Access Management (IAM) role that allows Amazon Forecast to access the location, 624 // and an AWS Key Management Service (KMS) key (optional). 625 Destination *DataDestination 626 627 // The last time the resource was modified. The timestamp depends on the status of 628 // the job: 629 // 630 // * CREATE_PENDING - The CreationTime. 631 // 632 // * CREATE_IN_PROGRESS - The 633 // current timestamp. 634 // 635 // * CREATE_STOPPING - The current timestamp. 636 // 637 // * CREATE_STOPPED 638 // - When the job stopped. 639 // 640 // * ACTIVE or CREATE_FAILED - When the job finished or 641 // failed. 642 LastModificationTime *time.Time 643 644 // Information about any errors that may have occurred during the backtest export. 645 Message *string 646 647 // The Amazon Resource Name (ARN) of the predictor backtest export job. 648 PredictorBacktestExportJobArn *string 649 650 // The name of the predictor backtest export job. 651 PredictorBacktestExportJobName *string 652 653 // The status of the predictor backtest export job. States include: 654 // 655 // * ACTIVE 656 // 657 // * 658 // CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED 659 // 660 // * CREATE_STOPPING, 661 // CREATE_STOPPED 662 // 663 // * DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED 664 Status *string 665} 666 667// The algorithm used to perform a backtest and the status of those tests. 668type PredictorExecution struct { 669 670 // The ARN of the algorithm used to test the predictor. 671 AlgorithmArn *string 672 673 // An array of test windows used to evaluate the algorithm. The 674 // NumberOfBacktestWindows from the object determines the number of windows in the 675 // array. 676 TestWindows []TestWindowSummary 677} 678 679// Contains details on the backtests performed to evaluate the accuracy of the 680// predictor. The tests are returned in descending order of accuracy, with the most 681// accurate backtest appearing first. You specify the number of backtests to 682// perform when you call the operation. 683type PredictorExecutionDetails struct { 684 685 // An array of the backtests performed to evaluate the accuracy of the predictor 686 // against a particular algorithm. The NumberOfBacktestWindows from the object 687 // determines the number of windows in the array. 688 PredictorExecutions []PredictorExecution 689} 690 691// Provides a summary of the predictor properties that are used in the 692// ListPredictors operation. To get the complete set of properties, call the 693// DescribePredictor operation, and provide the listed PredictorArn. 694type PredictorSummary struct { 695 696 // When the model training task was created. 697 CreationTime *time.Time 698 699 // The Amazon Resource Name (ARN) of the dataset group that contains the data used 700 // to train the predictor. 701 DatasetGroupArn *string 702 703 // The last time the resource was modified. The timestamp depends on the status of 704 // the job: 705 // 706 // * CREATE_PENDING - The CreationTime. 707 // 708 // * CREATE_IN_PROGRESS - The 709 // current timestamp. 710 // 711 // * CREATE_STOPPING - The current timestamp. 712 // 713 // * CREATE_STOPPED 714 // - When the job stopped. 715 // 716 // * ACTIVE or CREATE_FAILED - When the job finished or 717 // failed. 718 LastModificationTime *time.Time 719 720 // If an error occurred, an informational message about the error. 721 Message *string 722 723 // The ARN of the predictor. 724 PredictorArn *string 725 726 // The name of the predictor. 727 PredictorName *string 728 729 // The status of the predictor. States include: 730 // 731 // * ACTIVE 732 // 733 // * CREATE_PENDING, 734 // CREATE_IN_PROGRESS, CREATE_FAILED 735 // 736 // * DELETE_PENDING, DELETE_IN_PROGRESS, 737 // DELETE_FAILED 738 // 739 // * CREATE_STOPPING, CREATE_STOPPED 740 // 741 // The Status of the predictor 742 // must be ACTIVE before you can use the predictor to create a forecast. 743 Status *string 744} 745 746// The path to the file(s) in an Amazon Simple Storage Service (Amazon S3) bucket, 747// and an AWS Identity and Access Management (IAM) role that Amazon Forecast can 748// assume to access the file(s). Optionally, includes an AWS Key Management Service 749// (KMS) key. This object is part of the DataSource object that is submitted in the 750// CreateDatasetImportJob request, and part of the DataDestination object. 751type S3Config struct { 752 753 // The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an 754 // Amazon S3 bucket. 755 // 756 // This member is required. 757 Path *string 758 759 // The ARN of the AWS Identity and Access Management (IAM) role that Amazon 760 // Forecast can assume to access the Amazon S3 bucket or files. If you provide a 761 // value for the KMSKeyArn key, the role must allow access to the key. Passing a 762 // role across AWS accounts is not allowed. If you pass a role that isn't in your 763 // account, you get an InvalidInputException error. 764 // 765 // This member is required. 766 RoleArn *string 767 768 // The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key. 769 KMSKeyArn *string 770} 771 772// Defines the fields of a dataset. You specify this object in the CreateDataset 773// request. 774type Schema struct { 775 776 // An array of attributes specifying the name and type of each field in a dataset. 777 Attributes []SchemaAttribute 778} 779 780// An attribute of a schema, which defines a dataset field. A schema attribute is 781// required for every field in a dataset. The Schema object contains an array of 782// SchemaAttribute objects. 783type SchemaAttribute struct { 784 785 // The name of the dataset field. 786 AttributeName *string 787 788 // The data type of the field. 789 AttributeType AttributeType 790} 791 792// Provides statistics for each data field imported into to an Amazon Forecast 793// dataset with the CreateDatasetImportJob operation. 794type Statistics struct { 795 796 // For a numeric field, the average value in the field. 797 Avg *float64 798 799 // The number of values in the field. 800 Count *int32 801 802 // The number of distinct values in the field. 803 CountDistinct *int32 804 805 // The number of NAN (not a number) values in the field. 806 CountNan *int32 807 808 // The number of null values in the field. 809 CountNull *int32 810 811 // For a numeric field, the maximum value in the field. 812 Max *string 813 814 // For a numeric field, the minimum value in the field. 815 Min *string 816 817 // For a numeric field, the standard deviation. 818 Stddev *float64 819} 820 821// Describes a supplementary feature of a dataset group. This object is part of the 822// InputDataConfig object. Forecast supports the Weather Index and Holidays 823// built-in featurizations. Weather Index The Amazon Forecast Weather Index is a 824// built-in featurization that incorporates historical and projected weather 825// information into your model. The Weather Index supplements your datasets with 826// over two years of historical weather data and up to 14 days of projected weather 827// data. For more information, see Amazon Forecast Weather Index 828// (https://docs.aws.amazon.com/forecast/latest/dg/weather.html). Holidays Holidays 829// is a built-in featurization that incorporates a feature-engineered dataset of 830// national holiday information into your model. It provides native support for the 831// holiday calendars of 66 countries. To view the holiday calendars, refer to the 832// Jollyday (http://jollyday.sourceforge.net/data.html) library. For more 833// information, see Holidays Featurization 834// (https://docs.aws.amazon.com/forecast/latest/dg/holidays.html). 835type SupplementaryFeature struct { 836 837 // The name of the feature. Valid values: "holiday" and "weather". 838 // 839 // This member is required. 840 Name *string 841 842 // Weather Index To enable the Weather Index, set the value to "true" Holidays To 843 // enable Holidays, specify a country with one of the following two-letter country 844 // codes: 845 // 846 // * "AL" - ALBANIA 847 // 848 // * "AR" - ARGENTINA 849 // 850 // * "AT" - AUSTRIA 851 // 852 // * "AU" - 853 // AUSTRALIA 854 // 855 // * "BA" - BOSNIA HERZEGOVINA 856 // 857 // * "BE" - BELGIUM 858 // 859 // * "BG" - BULGARIA 860 // 861 // * 862 // "BO" - BOLIVIA 863 // 864 // * "BR" - BRAZIL 865 // 866 // * "BY" - BELARUS 867 // 868 // * "CA" - CANADA 869 // 870 // * "CL" - 871 // CHILE 872 // 873 // * "CO" - COLOMBIA 874 // 875 // * "CR" - COSTA RICA 876 // 877 // * "HR" - CROATIA 878 // 879 // * "CZ" - CZECH 880 // REPUBLIC 881 // 882 // * "DK" - DENMARK 883 // 884 // * "EC" - ECUADOR 885 // 886 // * "EE" - ESTONIA 887 // 888 // * "ET" - 889 // ETHIOPIA 890 // 891 // * "FI" - FINLAND 892 // 893 // * "FR" - FRANCE 894 // 895 // * "DE" - GERMANY 896 // 897 // * "GR" - 898 // GREECE 899 // 900 // * "HU" - HUNGARY 901 // 902 // * "IS" - ICELAND 903 // 904 // * "IN" - INDIA 905 // 906 // * "IE" - IRELAND 907 // 908 // * 909 // "IT" - ITALY 910 // 911 // * "JP" - JAPAN 912 // 913 // * "KZ" - KAZAKHSTAN 914 // 915 // * "KR" - KOREA 916 // 917 // * "LV" - 918 // LATVIA 919 // 920 // * "LI" - LIECHTENSTEIN 921 // 922 // * "LT" - LITHUANIA 923 // 924 // * "LU" - LUXEMBOURG 925 // 926 // * "MK" 927 // - MACEDONIA 928 // 929 // * "MT" - MALTA 930 // 931 // * "MX" - MEXICO 932 // 933 // * "MD" - MOLDOVA 934 // 935 // * "ME" - 936 // MONTENEGRO 937 // 938 // * "NL" - NETHERLANDS 939 // 940 // * "NZ" - NEW ZEALAND 941 // 942 // * "NI" - NICARAGUA 943 // 944 // * 945 // "NG" - NIGERIA 946 // 947 // * "NO" - NORWAY 948 // 949 // * "PA" - PANAMA 950 // 951 // * "PY" - PARAGUAY 952 // 953 // * "PE" - 954 // PERU 955 // 956 // * "PL" - POLAND 957 // 958 // * "PT" - PORTUGAL 959 // 960 // * "RO" - ROMANIA 961 // 962 // * "RU" - RUSSIA 963 // 964 // * 965 // "RS" - SERBIA 966 // 967 // * "SK" - SLOVAKIA 968 // 969 // * "SI" - SLOVENIA 970 // 971 // * "ZA" - SOUTH AFRICA 972 // 973 // * 974 // "ES" - SPAIN 975 // 976 // * "SE" - SWEDEN 977 // 978 // * "CH" - SWITZERLAND 979 // 980 // * "UA" - UKRAINE 981 // 982 // * "AE" - 983 // UNITED ARAB EMIRATES 984 // 985 // * "US" - UNITED STATES 986 // 987 // * "UK" - UNITED KINGDOM 988 // 989 // * "UY" - 990 // URUGUAY 991 // 992 // * "VE" - VENEZUELA 993 // 994 // This member is required. 995 Value *string 996} 997 998// The optional metadata that you apply to a resource to help you categorize and 999// organize them. Each tag consists of a key and an optional value, both of which 1000// you define. The following basic restrictions apply to tags: 1001// 1002// * Maximum number of 1003// tags per resource - 50. 1004// 1005// * For each resource, each tag key must be unique, and 1006// each tag key can have only one value. 1007// 1008// * Maximum key length - 128 Unicode 1009// characters in UTF-8. 1010// 1011// * Maximum value length - 256 Unicode characters in 1012// UTF-8. 1013// 1014// * If your tagging schema is used across multiple services and resources, 1015// remember that other services may have restrictions on allowed characters. 1016// Generally allowed characters are: letters, numbers, and spaces representable in 1017// UTF-8, and the following characters: + - = . _ : / @. 1018// 1019// * Tag keys and values are 1020// case sensitive. 1021// 1022// * Do not use aws:, AWS:, or any upper or lowercase combination 1023// of such as a prefix for keys as it is reserved for AWS use. You cannot edit or 1024// delete tag keys with this prefix. Values can have this prefix. If a tag value 1025// has aws as its prefix but the key does not, then Forecast considers it to be a 1026// user tag and will count against the limit of 50 tags. Tags with only the key 1027// prefix of aws do not count against your tags per resource limit. 1028type Tag struct { 1029 1030 // One part of a key-value pair that makes up a tag. A key is a general label that 1031 // acts like a category for more specific tag values. 1032 // 1033 // This member is required. 1034 Key *string 1035 1036 // The optional part of a key-value pair that makes up a tag. A value acts as a 1037 // descriptor within a tag category (key). 1038 // 1039 // This member is required. 1040 Value *string 1041} 1042 1043// The status, start time, and end time of a backtest, as well as a failure reason 1044// if applicable. 1045type TestWindowSummary struct { 1046 1047 // If the test failed, the reason why it failed. 1048 Message *string 1049 1050 // The status of the test. Possible status values are: 1051 // 1052 // * ACTIVE 1053 // 1054 // * 1055 // CREATE_IN_PROGRESS 1056 // 1057 // * CREATE_FAILED 1058 Status *string 1059 1060 // The time at which the test ended. 1061 TestWindowEnd *time.Time 1062 1063 // The time at which the test began. 1064 TestWindowStart *time.Time 1065} 1066 1067// The weighted loss value for a quantile. This object is part of the Metrics 1068// object. 1069type WeightedQuantileLoss struct { 1070 1071 // The difference between the predicted value and the actual value over the 1072 // quantile, weighted (normalized) by dividing by the sum over all quantiles. 1073 LossValue *float64 1074 1075 // The quantile. Quantiles divide a probability distribution into regions of equal 1076 // probability. For example, if the distribution was divided into 5 regions of 1077 // equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8. 1078 Quantile *float64 1079} 1080 1081// The metrics for a time range within the evaluation portion of a dataset. This 1082// object is part of the EvaluationResult object. The TestWindowStart and 1083// TestWindowEnd parameters are determined by the BackTestWindowOffset parameter of 1084// the EvaluationParameters object. 1085type WindowSummary struct { 1086 1087 // The type of evaluation. 1088 // 1089 // * SUMMARY - The average metrics across all windows. 1090 // 1091 // * 1092 // COMPUTED - The metrics for the specified window. 1093 EvaluationType EvaluationType 1094 1095 // The number of data points within the window. 1096 ItemCount *int32 1097 1098 // Provides metrics used to evaluate the performance of a predictor. 1099 Metrics *Metrics 1100 1101 // The timestamp that defines the end of the window. 1102 TestWindowEnd *time.Time 1103 1104 // The timestamp that defines the start of the window. 1105 TestWindowStart *time.Time 1106} 1107