# ============================================================================= # The code is originated from # Chen, M., Xu, Z., Zeng, A., & Xu, Q. (2023). "FrAug: Frequency Domain Augmentation for Time Series Forecasting". # arXiv preprint arXiv:2302.09292. # ============================================================================= import argparse import os import ast import torch from main_run.train import Exp_Main import random import numpy as np fix_seed = 2024 random.seed(fix_seed) torch.manual_seed(fix_seed) np.random.seed(fix_seed) TYPES = {0: 'None', 1: 'Freq-Mask', 2: 'Freq-Mix', 3: 'Wave-Mask', 4: 'Wave-Mix', 5: 'StAug'} parser = argparse.ArgumentParser(description='Augmentations for Time Series Forecasting') # basic config parser.add_argument('--model', type=str, required=True, default='DLinear', help='model name, options: [DLinear]') # data loader parser.add_argument('--is_training', type=int, required=True, default=1, help='status') parser.add_argument('--data', type=str, required=True, default='ETTh1', 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') parser.add_argument('--percentage', type=int, default=100, help='percentage of train data as a downsampling ratio') parser.add_argument('--patience', type=int, default=12, help='early stopping patience') # forecasting task parser.add_argument('--seq_len', type=int, default=336, help='input sequence length') parser.add_argument('--label_len', type=int, default=0, help='start token length') parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length') # DLinear parser.add_argument('--individual', action='store_true', default=False, help='DLinear: a linear layer for each variate(channel) individually') parser.add_argument('--enc_in', type=int, default=7, help='encoder input size') # DLinear with --individual, use this hyperparameter as the number of channels # 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=30, help='train epochs') parser.add_argument('--batch_size', type=int, default=64, help='batch size of train input data') 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('--lradj', type=str, default='type1', help='adjust learning rate') 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=False) parser.add_argument('--devices', type=str, default='0,1,2,3', help='device ids of multile gpus') parser.add_argument('--test_flop', action='store_true', default=False, help='See utils/tools for usage') # Augmentation parser.add_argument('--aug_type', type=int, default=0, help='0: No augmentation, 1: Frequency Masking 2: Frequency Mixing 3: Wave Masking 4: Wave Mixing 5: StAug ') parser.add_argument('--aug_rate', type=float, default=0.5, help='rate for FreqMask, FreqMix, and STAug') 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('--nIMF', type=int, default=500, help='number of IMFs for EMD (STAug)') parser.add_argument('--sampling_rate', type=float, default=0.5, help='sampling rate for WaveMask and WaveMix') 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) 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) for ii in range(args.itr): # setting record of experiments setting = '{}_{}_ft{}_sl{}_ll{}_pl{}_{}_{}_{}_{}'.format( args.model, args.data, args.features, args.seq_len, args.label_len, args.pred_len, args.des, args.aug_type, args.percentage, ii) exp = Exp(args) # set experiments print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting)) exp.train(setting) print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) mse, mae, rse = exp.test(setting) mse_avg[ii] = mse mae_avg[ii] = mae rse_avg[ii] = rse f = open("result-" + args.des + args.data + ".txt", 'a') f.write('\n') f.write('\n') f.write("-------START FROM HERE-----") f.write(TYPES[args.aug_type] + " \n") f.write('avg mse:{}, avg mae:{} avg rse:{} std mse:{}, std mae:{} std rse:{}'.format(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() torch.cuda.empty_cache() else: ii = 0 setting = '{}_{}_ft{}_sl{}_ll{}_pl{}_{}_{}_{}'.format(args.model, args.data, args.features, args.seq_len, args.label_len, args.pred_len, args.des, args.aug_type, ii) exp = Exp(args) # set experiments print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting)) exp.test(setting, test=1) torch.cuda.empty_cache()