Additional features about time-series-forecasting-pytorch

[jinglescode]

That's mostly data preparation, there are many ways to do it. Your aim is to include features in data_x_train and data_x_val, before creating the PyTorch Dataset at line 167:

dataset_train = TimeSeriesDataset(data_x_train, data_y_train)
dataset_val = TimeSeriesDataset(data_x_val, data_y_val)

You also need to remove at line 155:

x = np.expand_dims(x, 2)  # in our case, we have only 1 feature, so we need to convert `x` into [batch, sequence, features] for LSTM

[Dextergellizeau]

Hello Hong, Thanks for your quick response, however I am not getting it to work. I tried changing the following: Note fea is the dataset with the additional features. class Normalizer_Fea(): def __init__(self): self.mu = None self.sd = None def fit_transform_Fea(self, x): self.mu = np.mean(x, axis=(0), keepdims=True) self.sd = np.std(x, axis=(0), keepdims=True) normalized_x = (x - self.mu)/self.sd return normalized_x def inverse_transform_Fea(self, x): return (x*self.sd) + self.mu # normalize features scaler_fea = Normalizer_Fea() normalized_fea = scaler_fea.fit_transform_Fea(fea) def prepare_data_x(x, window_size): # perform windowing n_row = x.shape[0] - window_size + 1 output = np.lib.stride_tricks.as_strided(x, shape=(n_row, window_size), strides=(x.strides[0], x.strides[0])) return output[:-1], output[-1] def prepare_data_y(x, window_size): # # perform simple moving average # output = np.convolve(x, np.ones(window_size), 'valid') / window_size # use the next day as label output = x[window_size:] return output data_x, data_x_unseen = prepare_data_x(normalized_fea, window_size=config["data"]["window_size"]) data_y = prepare_data_y(normalized_data_close_price, window_size=config["data"]["window_size"]) split_index = int(data_y.shape[0]*config["data"]["train_split_size"]) data_x_train = data_x[:split_index, :] data_x_val = data_x[split_index:, :] data_y_train = data_y[:split_index] data_y_val = data_y[split_index:] Then I applied your changes and now receiving this error: RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x16 and 1x32) Regards, Dexter Gellizeau Sent from Mail<https://go.microsoft.com/fwlink/?LinkId=550986> for Windows 10 From: Hong Jing ***@***.***> Sent: Thursday, June 10, 2021 11:20 PM To: ***@***.***> Cc: ***@***.***>; ***@***.***> Subject: Re: [jinglescode/time-series-forecasting-pytorch] Additional features (#5) That's mostly data preparation, there are many ways to do it. Your aim is to include features in data_x_train and data_x_val, before creating the PyTorch Dataset at line 167<https://github.com/jinglescode/time-series-forecasting-pytorch/blob/main/project.py#L167>: dataset_train = TimeSeriesDataset(data_x_train, data_y_train) dataset_val = TimeSeriesDataset(data_x_val, data_y_val) You also need to remove at line 155<https://github.com/jinglescode/time-series-forecasting-pytorch/blob/main/project.py#L155>: x = np.expand_dims(x, 2) # in our case, we have only 1 feature, so we need to convert `x` into [batch, sequence, features] for LSTM — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub<#5 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AUN6P7U2UQPGBOEQLX6CVN3TSF6IVANCNFSM46PT6MFA>.

[Piyush-Bijwal]

hi @Dextergellizeau , were you able to get the code running? i'm also trying to do the same but not able to get the code running.
Regards,
Piyush