I tried running the code and installing dependencies using pip and pip3 and I tried running the code using python and python3, but I keep getting this:

Using TensorFlow backend.
> Loading data...
> Data Loaded. Compiling...
Traceback (most recent call last):
  File "run.py", line 36, in <module>
    model = lstm.build_model([1, 50, 100, 1])
  File "/root/LSTM/lstm.py", line 53, in build_model
    return_sequences=True))
  File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 87, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/layers/recurrent.py", line 931, in __init__
    super(LSTM, self).__init__(**kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/layers/recurrent.py", line 181, in __init__
    super(Recurrent, self).__init__(**kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 275, in __init__
    raise TypeError('Keyword argument not understood:', kwarg)
TypeError: ('Keyword argument not understood:', 'input_dim')

Hello,

I have a dataset of raining time in a year. the dataset is something like this:

Time       Raining      Wind-speed         Temp
10            23           50               22
14            10           34               20
15             8           23               18

.......

magnitudes are just as an example. as you see time intervals are not fixed. I want to predict the next raining time by giving a previous time. But all examples I found about LSTM had fixed increasing time steps. for example 10,11,12,13,14 ...., such as stock market or sinus wave

But in my problem time steps are not fixed, but the time has an increasing rate. starts with a number and increases but the incident steps are not fixed. What should I do in these problems because I want to predict the next raining time

Using the latest code base, a ValueError is thrown after training the first epoch.

• Python 3.5.2
• TensorFlow 1.10.1
• Numpy 1.14.0
• Keras 2.2.2
• Matplotlib 2.1.2

[Model] Model Compiled
Time taken: 0:00:00.809316
[Model] Training Started
[Model] 2 epochs, 32 batch size, 124 batches per epoch
Epoch 1/2
124/124 [==============================] - 45s 363ms/step - loss: 0.0022
Epoch 2/2
  1/124 [..............................] - ETA: 36s - loss: 6.4717e-04
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-27-e3c645699881> in <module>()
     76 
     77 if __name__=='__main__':
---> 78     main()

<ipython-input-27-e3c645699881> in main()
     58         batch_size = configs['training']['batch_size'],
     59         steps_per_epoch = steps_per_epoch,
---> 60         save_dir = configs['model']['save_dir']
     61     )
     62 

/notebooks/storage/core/model.py in train_generator(self, data_gen, epochs, batch_size, steps_per_epoch, save_dir)
     81                         epochs=epochs,
     82                         callbacks=callbacks,
---> 83                         workers=1
     84 		)
     85 

/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1413             use_multiprocessing=use_multiprocessing,
   1414             shuffle=shuffle,
-> 1415             initial_epoch=initial_epoch)
   1416 
   1417     @interfaces.legacy_generator_methods_support

/usr/local/lib/python3.5/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    211                 outs = model.train_on_batch(x, y,
    212                                             sample_weight=sample_weight,
--> 213                                             class_weight=class_weight)
    214 
    215                 outs = to_list(outs)

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
   1207             x, y,
   1208             sample_weight=sample_weight,
-> 1209             class_weight=class_weight)
   1210         if self._uses_dynamic_learning_phase():
   1211             ins = x + y + sample_weights + [1.]

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    747             feed_input_shapes,
    748             check_batch_axis=False,  # Don't enforce the batch size.
--> 749             exception_prefix='input')
    750 
    751         if y is not None:

/usr/local/lib/python3.5/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    125                         ': expected ' + names[i] + ' to have ' +
    126                         str(len(shape)) + ' dimensions, but got array '
--> 127                         'with shape ' + str(data_shape))
    128                 if not check_batch_axis:
    129                     data_shape = data_shape[1:]

ValueError: Error when checking input: expected lstm_1_input to have 3 dimensions, but got array with shape (8, 1)

Did some debugging using the example config for stock market prediction. For each step in epoch 1, generate_train_batch yields two arrays x_batch and y_batch of shape (32, 49, 1) and (32, 1) respectively.

At the start of epoch 2, model.fit_generator requests a batch of train data from the generator which is now exhausted. At this point generate_train_batch yields arrays of shape (17,) and (17, 1) which causes model.fit_generator to throw an error.

According to The Sequential model API, model.fit_generator expects the generator to loop over its data indefinitely. An epoch finishes when steps_per_epoch batches have been seen by the model. However, the generate_train_batch implementation stops yielding data after one epoch (as described above).

Hi,

I installed all the requirements (pip -r requirements.txt) , using python3 (tried 3.5 and 3.6). This is debian buster, but I have tried many dockers.

Using TensorFlow backend. [Model] Model Compiled Time taken: 0:00:00.955068 Traceback (most recent call last): File "run.py", line 84, in main() File "run.py", line 46, in main normalise = configs['data']['normalise'] File "/tmp/LSTM-Neural-Network-for-Time-Series-Prediction/core/data_processor.py", line 44, in get_train_data x, y = self._next_window(i, seq_len, normalise) File "/tmp/LSTM-Neural-Network-for-Time-Series-Prediction/core/data_processor.py", line 68, in _next_window window = self.normalise_windows(window, single_window=True)[0] if normalise else window File "/tmp/LSTM-Neural-Network-for-Time-Series-Prediction/core/data_processor.py", line 78, in normalise_windows normalised_window = [((float(p) / float(window[0])) - 1) for p in window] File "/tmp/FinanceModels/LSTM-Neural-Network-for-Time-Series-Prediction/core/data_processor.py", line 78, in normalised_window = [((float(p) / float(window[0])) - 1) for p in window] TypeError: only size-1 arrays can be converted to Python scalars

Anyone out there get the standard example to work?

NT.

Traceback (most recent call last): File "/Users/gsk/Desktop/LSTM-Neural-Network-for-Time-Series-Prediction/run.py", line 32, in X_train, y_train, X_test, y_test = lstm.load_data('ok.csv', seq_len, True) File "/Users/gsk/Desktop/LSTM-Neural-Network-for-Time-Series-Prediction/lstm.py", line 23, in load_data result = normalise_windows(result) File "/Users/gsk/Desktop/LSTM-Neural-Network-for-Time-Series-Prediction/lstm.py", line 43, in normalise_windows normalised_window = [((float(p) / float(window[0])) - 1) for p in window] ValueError: could not convert string to float:

I read the corresponding article (completely). http://www.jakob-aungiers.com/articles/a/LSTM-Neural-Network-for-Time-Series-Prediction

But I can't get it, what normalizing in this example does. Could someone explain it any further?

When I run the code,I meet the following problems: Traceback (most recent call last): File "run.py", line 36, in model = lstm.build_model([1, 50, 100, 1]) File "/home/cuiyi/Downloads/SPARNN-release/data/LSTM/LSTM-Neural-Network-for-Time-Series-Prediction/lstm.py", line 51, in build_model return_sequences=True)) File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 276, in add layer.create_input_layer(batch_input_shape, input_dtype) File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 370, in create_input_layer self(x) File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 487, in call self.build(input_shapes[0]) File "/usr/local/lib/python2.7/dist-packages/keras/layers/recurrent.py", line 710, in build self.W = K.concatenate([self.W_i, self.W_f, self.W_c, self.W_o]) File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 716, in concatenate return tf.concat(axis, [to_dense(x) for x in tensors]) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1047, in concat dtype=dtypes.int32).get_shape( File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 651, in convert_to_tensor as_ref=False) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 716, in internal_convert_to_tensor ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function return constant(v, dtype=dtype, name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape)) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto _AssertCompatible(values, dtype) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible (dtype.name, repr(mismatch), type(mismatch).name)) TypeError: Expected int32, got <tensorflow.python.ops.variables.Variable object at 0x7f3d6a773a10> of type 'Variable' instead.

I've tried to reproduce the sine wave example using the following code in the main function seen below.

Apparently, something went wrong, see the image I have received.

Do you know what I need to fix in order to reproduce your example?

Thanks!

#Main Run Thread if name=='main':

os.system('clear')
global_start_time = time.time()
epochs  = 1
seq_len = 50

print '> Loading data... '

X_train, y_train, X_test, y_test = lstm.load_data('sinwave.csv', seq_len, True)

print '> Data Loaded. Compiling...'

model = lstm.build_model([1, 50, 100, 1])

model.fit(
    X_train,
    y_train,
    batch_size=512,
    nb_epoch=epochs,
    validation_split=0.05)

predicted = lstm.predict_point_by_point(model, X_test)        

print 'Training duration (s) : ', time.time() - global_start_time
plot_results(predicted, y_train)

Hello Jakob, Thank you for this inspiring, well written piece of code !

I noticed results differ when using in-memory training & out-of-memory generative training. I did not modify the code otherwise in any other file than run.py: I'm just commenting the "out-of-memory generative traning" block and un-commenting the block "in-memory training" and results are the following:

Results without any modification (very similar to what you posted in your article, just another run without fixed seeds):

Results using in-memory training:

Looking at the code in file model.py I can't understand why in-memory training produces these results. Is this normal ? Could generative training produce much better results, as we have here ?

I also modified your code to check performance for binary up/down price prediction, and results are also better using generative training. I'm wondering what is happening here.

Thank you again for sharing your code, it really is the best software & article I could find about LSTM for stock prediction.

generate_train_batch is already add "while True:" but, train raise error. ValueError: Error when checking input: expected lstm_1_input to have 3 dimensions, but got array with shape (8, 1)

Hello, In the case of the sine wave, we are sampling the wave at equal "time" or x intervals. However, since its periodic, wouldn't that mean that test data is essentially a copy of the training data ? Would that be a valid method of testing though ?

Thanks !

Bumps tensorflow-gpu from 1.10.0 to 2.9.3.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Two points in my view could make the project more perfect. Welcome discussion and correction if I was wrong.

1. When scrolling the window, the last one timestep is forgotten. It could add 1 the for loop in line26 and line45 to add 1 sample, like this "range(self.len_test - seq_len+1)".
2. Rather than scrolling the window in train and test dataset respectively, I think it could be better scrolling the window first and split the train and test dataset. This could add 50 (one seq_len) samples.

https://github.com/DataCanvasIO/HyperTS

Easy-to-use, powerful, unified full pipeline automated time series toolkit. Supports forecasting, classification and regression.

Bumps numpy from 1.15.0 to 1.22.0.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

WARNING: QApplication was not created in the main() thread. [2022-03-06 16:11:07,935] ERROR in app: Exception on /insertintotable [POST] Traceback (most recent call last): File "D:\Programs\Python\Python310\lib\site-packages\flask\app.py", line 2073, in wsgi_app response = self.full_dispatch_request() File "D:\Programs\Python\Python310\lib\site-packages\flask\app.py", line 1518, in full_dispatch_request rv = self.handle_user_exception(e) File "D:\Programs\Python\Python310\lib\site-packages\flask\app.py", line 1516, in full_dispatch_request rv = self.dispatch_request() File "D:\Programs\Python\Python310\lib\site-packages\flask\app.py", line 1502, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**req.view_args) File "D:\Downloads\Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis-master\Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis-master\main.py", line 474, in insertintotable arima_pred, error_arima=ARIMA_ALGO(df) File "D:\Downloads\Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis-master\Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis-master\main.py", line 118, in ARIMA_ALGO predictions = arima_model(train, test) File "D:\Downloads\Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis-master\Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis-master\main.py", line 93, in arima_model model = ARIMA(history, order=(6,1 ,0)) File "D:\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\arima_model.py", line 45, in init super().init(*args, **kwargs) File "D:\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\arima_model.py", line 29, in init raise NotImplementedError(ARIMA_DEPRECATION_ERROR) NotImplementedError: statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have been removed in favor of statsmodels.tsa.arima.model.ARIMA (note the . between arima and model) and statsmodels.tsa.SARIMAX.

statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and is both well tested and maintained. It also offers alternative specialized parameter estimators.