# Adapted from dominant-shuffle (https://github.com/zuojie2024/dominant-shuffle) and FrAug implementations # Original works: Dominant Shuffle by Kai Zhao et al., FrAug by Muxi Chen et al. # Modified and extended by Jafar Bakhshaliyev (2025) import argparse import os import torch from exp.exp_main import Exp_Main import ast import random import numpy as np #def setup_seed(seed): # torch.manual_seed(seed) # torch.cuda.manual_seed_all(seed) # torch.cuda.manual_seed(seed) # np.random.seed(seed) # random.seed(seed) # torch.backends.cudnn.deterministic = True #setup_seed(2021) parser = argparse.ArgumentParser(description='Data Augmentations for Time Series Forecasting') # basic config parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast', help='task name, options:[long_term_forecast, short_term_forecast]') parser.add_argument('--is_training', type=int, required=True, default=1, help='status') parser.add_argument('--model_id', type=str, required=True, default='test', help='model id') parser.add_argument('--model', type=str, required=True, default='Autoformer', help='model name, options: [Autoformer, Informer, Transformer]') # data loader parser.add_argument('--data', type=str, required=True, default='ETTm1', help='dataset type') parser.add_argument('--root_path', type=str, default='./dataset/', help='root path of the data file') parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file') parser.add_argument('--features', type=str, default='M', help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate') parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task') parser.add_argument('--freq', type=str, default='h', help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h') parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints') # forecasting task parser.add_argument('--seq_len', type=int, default=96, help='input sequence length') parser.add_argument('--label_len', type=int, default=48, help='start token length') parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length') parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4') # MICN parser.add_argument('--conv_kernel', type=int, nargs='+', default=[17,49], help='downsampling and upsampling convolution kernel_size') parser.add_argument('--decomp_kernel', type=int, nargs='+', default=[17,49], help='decomposition kernel_size') parser.add_argument('--isometric_kernel', type=int, nargs='+', default=[17,49], help='isometric convolution kernel_size') parser.add_argument('--mode', type=str, default='regre', help='different mode of trend prediction block: [regre or mean]') parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False) # LightTS parser.add_argument('--chunk_size', type=int, default=40, help='LightTS') # PatchTST parser.add_argument('--fc_dropout', type=float, default=0.05, help='fully connected dropout') parser.add_argument('--head_dropout', type=float, default=0.0, help='head dropout') parser.add_argument('--patch_len', type=int, default=16, help='patch length') parser.add_argument('--stride', type=int, default=8, help='stride') parser.add_argument('--padding_patch', default='end', help='None: None; end: padding on the end') parser.add_argument('--revin', type=int, default=1, help='RevIN; True 1 False 0') parser.add_argument('--affine', type=int, default=0, help='RevIN-affine; True 1 False 0') parser.add_argument('--subtract_last', type=int, default=0, help='0: subtract mean; 1: subtract last') parser.add_argument('--decomposition', type=int, default=0, help='decomposition; True 1 False 0') parser.add_argument('--kernel_size', type=int, default=25, help='decomposition-kernel') parser.add_argument('--individual', type=int, default=0, help='individual head; True 1 False 0') # SCINet parser.add_argument('--hidden_size', default=1, type=float, help='hidden channel of module') parser.add_argument('--kernel', default=5, type=int, help='kernel size, 3, 5, 7') parser.add_argument('--groups', type=int, default=1) parser.add_argument('--levels', type=int, default=3) parser.add_argument('--stacks', type=int, default=1, help='1 stack or 2 stacks') parser.add_argument('--top_k', type=int, default=5) # Formers parser.add_argument('--num_kernels', type=int, default=6, help='for Inception') parser.add_argument('--embed_type', type=int, default=0, help='0: default 1: value embedding + temporal embedding + positional embedding 2: value embedding + temporal embedding 3: value embedding + positional embedding 4: value embedding') parser.add_argument('--enc_in', type=int, default=7, help='encoder input size') parser.add_argument('--dec_in', type=int, default=7, help='decoder input size') parser.add_argument('--c_out', type=int, default=7, help='output size') parser.add_argument('--d_model', type=int, default=512, help='dimension of model') parser.add_argument('--n_heads', type=int, default=8, help='num of heads') parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers') parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers') parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn') parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average') parser.add_argument('--factor', type=int, default=1, help='attn factor') parser.add_argument('--distil', action='store_false', help='whether to use distilling in encoder, using this argument means not using distilling', default=True) parser.add_argument('--dropout', type=float, default=0.05, help='dropout') parser.add_argument('--embed', type=str, default='timeF', help='time features encoding, options:[timeF, fixed, learned]') parser.add_argument('--activation', type=str, default='gelu', help='activation') parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder') parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data') # CycleNet parser.add_argument('--cycle', type=int, default=24, help='cycle length') parser.add_argument('--model_type', type=str, default='mlp', help='model type, options: [linear, mlp]') parser.add_argument('--use_revin', type=int, default=1, help='1: use revin or 0: no revin') # optimization parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers') parser.add_argument('--itr', type=int, default=2, help='experiments times') parser.add_argument('--train_epochs', type=int, default=10, help='train epochs') #default = 10 parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data') parser.add_argument('--patience', type=int, default=5, help='early stopping patience') parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate') parser.add_argument('--des', type=str, default='test', help='exp description') parser.add_argument('--loss', type=str, default='mse', help='loss function') parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate') parser.add_argument('--pct_start', type=float, default=0.3, help='pct_start') parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False) # GPU parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu') parser.add_argument('--gpu', type=int, default=0, help='gpu') parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=True) parser.add_argument('--devices', type=str, default='0', help='device ids of multile gpus') #default='0,1,2,3' parser.add_argument('--test_flop', action='store_true', default=False, help='See utils/tools for usage') # Augmentation parser.add_argument('--aug_method', type=str, default='f_mask', help='f_mask: Frequency Masking, f_mix: Frequency Mixing') parser.add_argument('--aug_rate', type=float, default=0.5, help='mask/mix rate/ shuffle rate') parser.add_argument('--in_batch_augmentation', action='store_true', help='Augmentation in Batch (save memory cost)', default=False) parser.add_argument('--in_dataset_augmentation', action='store_true', help='Augmentation in Dataset', default=False) parser.add_argument('--closer_data_aug_more', action='store_true', help='Augment times increase for data closer to test set', default=False) parser.add_argument('--data_size', type=float, default=1, help='size of dataset, i.e, 0.01 represents uses 1 persent samples in the dataset') parser.add_argument('--aug_data_size', type=int, default=1, help='size of augmented data, i.e, 1 means double the size of dataset') parser.add_argument('--seed', type=int, default=2021, help='random seed') parser.add_argument('--wo_original_set', action='store_true', help='without original train set') parser.add_argument('--test_time_train', type=bool, default=False, help='Affect data division') parser.add_argument('--wavelet', type=str, default='db2', help='wavelet form for DWT') parser.add_argument('--level', type=int, default=2, help='level for DWT') parser.add_argument('--rates', type=str, default="[0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]", help='List of float rates as a string, e.g., "[0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]"') parser.add_argument('--sampling_rate', type=float, default=0.5, help='sampling rate for WaveMask and WaveMix') parser.add_argument('--uniform', action='store_true', help='Uniform rates for wavemask/mix', default=False) parser.add_argument('--n_patch', type=int, default=4, help='# of patches') parser.add_argument('--aug_stride', type=int, default=5, help='# of patches stride') parser.add_argument('--aug_patch_len', type=int, default=5, help='# of patches') parser.add_argument('--warp_scale', type=float, default=0.2, help='# of pkjatches') parser.add_argument('--shuffle', action='store_true', help='Uniform rates for wavemask/mix', default=False) parser.add_argument('--block_size', type=int, default=8, help='block size of MBB') parser.add_argument('--K_num', type=int, default=1, help='K number of RobustTAD') parser.add_argument('--seg_ratio', type=float, default=0.2, help='segment ratio of RobustTAD') parser.add_argument('--use_PEMSmetric', action='store_true', help='use PEMS metric', default=False) parser.add_argument('--use_former', action='store_true', help='use fomer', default=False) args = parser.parse_args() args.rates = ast.literal_eval(args.rates) args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False if args.use_gpu and args.use_multi_gpu: args.dvices = args.devices.replace(' ', '') device_ids = args.devices.split(',') args.device_ids = [int(id_) for id_ in device_ids] args.gpu = args.device_ids[0] print('Args in experiment:') print(args) if args.task_name == 'long_term_forecast': Exp = Exp_Main # Exp = Exp_Main if args.is_training: mse_avg, mae_avg, rse_avg = np.zeros(args.itr), np.zeros(args.itr), np.zeros(args.itr) val_mse_avg = np.zeros(args.itr) for ii in range(args.itr): # setting record of experiments setting = '{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format( args.task_name, args.model_id, args.model, args.data, args.features, args.seq_len, args.label_len, args.pred_len, args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.factor, args.embed, args.distil, args.des, ii) exp = Exp(args) # set experiments print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting)) _, val_loss = exp.train(setting) val_mse_avg[ii] = val_loss print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) mse, mae, rse = exp.test(setting) mse_avg[ii] = mse mae_avg[ii] = mae rse_avg[ii] = rse if args.do_predict: print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) exp.predict(setting, True) torch.cuda.empty_cache() f = open("result-" + args.des + args.data + ".txt", 'a') f.write('\n') f.write('\n') f.write("-------START FROM HERE-----") f.write(args.aug_method + " " + str(args.pred_len) +" \n") f.write('avg val mse:{}, std val mse:{}, avg mse:{}, avg mae:{} avg rse:{} std mse:{}, std mae:{} std rse:{}'.format(val_mse_avg.mean(), val_mse_avg.std(), mse_avg.mean(), mae_avg.mean(), rse_avg.mean(), mse_avg.std(), mae_avg.std(), rse_avg.std())) f.write('\n') f.write('\n') f.close() else: ii = 0 setting = '{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_fc{}_eb{}_dt{}_{}_{}'.format(args.model_id, args.model, args.data, args.features, args.seq_len, args.label_len, args.pred_len, args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.factor, args.embed, args.distil, args.des, ii) exp = Exp(args) # set experiments print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) exp.test(setting, test=1) torch.cuda.empty_cache()