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params.json
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params.json
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{
"dataset": {
"type": "str",
"help": "Name of the dataset",
"default": "weekly_sales"},
"modelToRun": {
"type": "str",
"help": "Forecasting model - baseline,aru,adapt,hierarchical,hybrid,attention",
"default": "baseline"},
"rnnType": {
"type": "str",
"help": "Type of the RNN cell - lstm, sru, cudnn_lstm",
"default": "lstm"},
"decoderType": {
"type": "str",
"help": "Type of the decoder cell - ffnn, rnn",
"default": "ffnn"},
"isSRUFeat": {
"type": "bool",
"help": "Use SRU features, boolean",
"default": 0},
"isLagFeat": {
"type": "bool",
"help": "Use Lag features, boolean",
"default": 0},
"isTsId": {
"type": "bool",
"help": "Use Lag features, boolean",
"default": 0},
"isPrependZeros": {
"type": "bool",
"help": "Prepend data with zeros such that entire prediction range is covered, boolean",
"default": 0},
"encoder_length": {
"type": "int",
"help": "Encoder Length",
"default": 8},
"decoder_length": {
"type": "int",
"help": "Decoder Length",
"default": 8},
"test_length": {
"type": "int",
"help": "Length of test sequence",
"default": 8},
"val_length": {
"type": "int",
"help": "Length of test sequence",
"default": 8},
"testFraction": {
"type": "float",
"help": "Fraction of sequence to use as test sequence",
"default": 0},
"testView": {
"type": "int",
"help": "Select test set 1 or test set 2",
"default": 1},
"isAdapt": {
"type": "bool",
"help": "Perform local adaptation, boolean",
"default": 0},
"hidden1": {
"type": "int",
"help": "No of hidden units in first hidden layer",
"default": 64},
"hidden2": {
"type": "int",
"help": "No of hidden units in second hidden layer",
"default": 32},
"learning_rate": {
"type": "float",
"help": "Learning rate for adam optimizer",
"default": 0.0001},
"num_nodes": {
"type": "int",
"help": "No of hidden units in RNN cell",
"default": 20},
"num_layers": {
"type": "int",
"help": "No of Recurrent layers",
"default": 1},
"lstm_dense_layer_size": {
"type": "int",
"help": "No of units in dense layer over RNN state.",
"default": 5},
"batch_size": {
"type": "int",
"help": "Batch size",
"default": 120},
"num_iterations": {
"type": "int",
"help": "No of iterations ",
"default": 150},
"sru_num_stats": {
"type": "int",
"help": "No of states in SRU cell",
"default": 1500},
"recur_dims": {
"type": "int",
"help": "same as recur_dims of SRU cell",
"default": 60},
"stepLen": {
"type": "int",
"help": "No of steps to slide while creating windows",
"default": 1},
"seed": {
"type": "int",
"help": "seed for initializations",
"default": 12},
"numProjs": {
"type": "int",
"help": "number of projections in ARU",
"default": 0},
"fullLinear": {
"type": "bool",
"help": "Use Multivariate linear model in ARU",
"default": 0},
"numY": {
"type": "int",
"help": "number of derived y s for ARU",
"default":1},
"gtVsPredOutputFile": {
"type": "str",
"help": "Name of ouptut file to store ground-truth and predicted values",
"default": "output.pkl"},
"isLogNormalised": {
"type": "bool",
"help": "Is the data log normalized?",
"default": 0},
"isNormalised": {
"type": "bool",
"help": "Normalize the data, boolean",
"default": 0},
"deep_ar_normalize": {
"type": "bool",
"help": "Normalize Y",
"default": 0},
"aru_deepar_normalize": {
"type": "bool",
"help": "Normalize ARU output",
"default": 0},
"deep_ar_normalize_seq": {
"type": "bool",
"help": "Normalize Y across full sequence",
"default": 0},
"aruRegularizer": {
"type": "float",
"help": "Regularization parameter for ARU",
"default": 0.01},
"verbose": {
"type": "bool",
"help": "Is verbose",
"default": 0},
"is_maru_loss": {
"type": "bool",
"help": "Use extra loss term for modified ARU",
"default": 0},
"is_probabilistic_pred": {
"type": "bool",
"help": "Use extra loss term for modified ARU",
"default": 0},
"aru_alpha": {
"type": "str",
"help": "Aging for ARU states",
"default": "[0.01, 0.2, 0.9, 1.0]" },
"valFrom": {
"type": "str",
"help": "Pick validation data from train/test",
"default": ""},
"noFeat": {
"type": "bool",
"help": "Do not use any external features",
"default": ""},
"synth_seq_len": {
"type": "int",
"help": "Sequence length for synthetic data",
"default": 100},
"synth_num_ts": {
"type": "int",
"help": "Number of timeseries for synthetic data",
"default": 10}
}