毕业设计带论文-基于特征工程和xgboost的运力预测，大宗货运场景下的运力供需预测python源码+文档说明+论文+答辩ppt

### This repository encapsulates the process of modeling and forecasting of time series based deep learning,making it super easy to use. ### Quick start ```python train_file_path = r"dataset/pollution.csv" #data is univariate tite series data = pd.read_csv(train_file_path, header=0, index_col=0)["pollution"].values #get train_data and test_data train_data, test_data = divide_train_test(data) #initialise model parameters ts = ts_model() # fit and predict preds, reals = ts.fit_transform(train_data, test_data) # plot prediction result ts.plot_predict_result(preds, reals) ``` #### 1. model parameters Some model parameters are diagrammed below：  | The meaning of model parameters | | ------------------------------------------------------------ | | wnd_len : int ,default=24 | | the length of sliding window.The sequence in sliding window is the single input sequence of LSTM. | | pred_len : int ,default=24 | | prediction sequence length | | net : str ,default=LSTM | | Net to model time series.Choices include 'LSTM' or 'RNN' or "GRU" | | rnn_input_size : int ,default=8 | | embedding dimension,used to map input feature(for univariate,feature is one) to a high dimension | | rnn_hid_size : int ,default=64. | | the dimension of hidden layer in RNN,LSTM,GRU | | batch_size : int, default=32 | | Batch size to use during SGD optimization. | | lr : float, default=1E-3 | | Learning rate used for optimization. | | n_epochs : int, default=299 | | Number of epochs to use during optimization. | | optimizer : str, default='Adam' | | Optimizer to use during SGD optimization. Choices include 'Adam' or 'SGD'. | | criterion : str, default='MSELoss' | | Prediction loss function to use. | | train_proportion : float,default=0.8 | | the proportion of train data used to train model.The rest part is used for validation | | not_use_visdom : bool,default=True | | not use visdom to visualize the loss in training process,when False ensure run"python -m visdom.server" firstly | | cuda : bool, default=False | | Whether or not to use CUDA. | | dropout_rate : float, default=0.5. | | Dropout rate for hidden layers. | | verbose : bool, default=True | | Print out loss information. | #### 2. model method | fit_transform(train_data,test_data) | train model on train_data and predict on test_data | | ----------------------------------- | -------------------------------------------------- | | fit(train_data) | train model on train_data | | transform(test_data) | predict on test_data | | plot_predict_result(preds, real) | plot prediction result | | save_best_model(save_path): | save file on specified path | | get_min_loss() | return minimum validation loss | If you have any questions, please open an issue.