问题：
如何让Keras LSTM在多元设置中对多个时间序列进行预测？
公羊新
我试图使用Keras对多个变量进行同时预测。在这里使用这个例子，我想预测所有特征的值，包括pm 2.5，DEWP，TEMP等，而不仅仅是污染（pm 2.5）。本质上，这是，给定所有变量，建立一个模型来预测所有变量作为时间序列，而不仅仅是预测一个变量。
我使用重塑后的3D数据修改了原始示例代码，但出现了一个错误。代码如下：
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from pandas import read_csv, DataFrame, concat
from datetime import datetime
# load data
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)
# manually specify column names
dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'
# mark all NA values with 0
dataset['pollution'].fillna(0, inplace=True)
# drop the first 24 hours
dataset = dataset[24:]
# summarize first 5 rows
print(dataset.head(5))
# save to file
dataset.to_csv('pollution.csv')
# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# put it all together
agg = concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# integer encode direction
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
# frame as supervised learning
reframed = series_to_supervised(scaled, 1, 1)
# split into train and test sets
values = reframed.values
n_train_hours = 365 * 24
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]
# split into input and outputs
train_X, train_y = train[:, :-8], train[:, -8:]
test_X, test_y = test[:, :-8], test[:, -8:]
# reshape input to be 3D [samples, timesteps, features]
train_X_3d = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X_3d = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
train_y_3d = train_y.reshape((train_y.shape[0], 1, train_y.shape[1]))
test_y_3d = test_y.reshape((test_y.shape[0], 1, test_y.shape[1]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
print(train_X_3d.shape, train_y_3d.shape, test_X_3d.shape, test_y_3d.shape)
# design network
model = Sequential()
model.add(LSTM(50, input_shape=(train_X_3d.shape[1], train_X_3d.shape[2])))
#model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X_3d, train_y_3d,
epochs=50, batch_size=72,
validation_data=(test_X_3d, test_y_3d), verbose=2, shuffle=False)
# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()
# make a prediction
yhat = model.predict(test_X_3d)
yhat
输出为：
pollution dew temp press wnd_dir wnd_spd snow rain
date
2010-01-02 00:00:00 129.0 -16 -4.0 1020.0 SE 1.79 0 0
2010-01-02 01:00:00 148.0 -15 -4.0 1020.0 SE 2.68 0 0
2010-01-02 02:00:00 159.0 -11 -5.0 1021.0 SE 3.57 0 0
2010-01-02 03:00:00 181.0 -7 -5.0 1022.0 SE 5.36 1 0
2010-01-02 04:00:00 138.0 -7 -5.0 1022.0 SE 6.25 2 0
(8760, 8) (8760, 8) (35039, 8) (35039, 8)
(8760, 1, 8) (8760, 1, 8) (35039, 1, 8) (35039, 1, 8)
Train on 8760 samples, validate on 35039 samples
Epoch 1/50
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1609 try:
-> 1610 c_op = c_api.TF_FinishOperation(op_desc)
1611 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimensions must be equal, but are 50 and 8 for 'loss/lstm_loss/sub' (op: 'Sub') with input shapes: [?,50], [?,1,8].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-1-00c079ab5854> in <module>
97 history = model.fit(train_X_3d, train_y_3d,
98 epochs=50, batch_size=72,
---> 99 validation_data=(test_X_3d, test_y_3d), verbose=2, shuffle=False)
100 # plot history
101 pyplot.plot(history.history['loss'], label='train')
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in distributed_function(input_iterator)
71 strategy = distribution_strategy_context.get_strategy()
72 outputs = strategy.experimental_run_v2(
---> 73 per_replica_function, args=(model, x, y, sample_weights))
74 # Out of PerReplica outputs reduce or pick values to return.
75 all_outputs = dist_utils.unwrap_output_dict(
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
758 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
759 convert_by_default=False)
--> 760 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
761
762 def reduce(self, reduce_op, value, axis):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1785 kwargs = {}
1786 with self._container_strategy().scope():
-> 1787 return self._call_for_each_replica(fn, args, kwargs)
1788
1789 def _call_for_each_replica(self, fn, args, kwargs):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2130 self._container_strategy(),
2131 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2132 return fn(*args, **kwargs)
2133
2134 def _reduce_to(self, reduce_op, value, destinations):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
262 y,
263 sample_weights=sample_weights,
--> 264 output_loss_metrics=model._output_loss_metrics)
265
266 if reset_metrics:
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
309 sample_weights=sample_weights,
310 training=True,
--> 311 output_loss_metrics=output_loss_metrics))
312 if not isinstance(outs, list):
313 outs = [outs]
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
250 output_loss_metrics=output_loss_metrics,
251 sample_weights=sample_weights,
--> 252 training=training))
253 if total_loss is None:
254 raise ValueError('The model cannot be run '
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _model_loss(model, inputs, targets, output_loss_metrics, sample_weights, training)
164
165 if hasattr(loss_fn, 'reduction'):
--> 166 per_sample_losses = loss_fn.call(targets[i], outs[i])
167 weighted_losses = losses_utils.compute_weighted_loss(
168 per_sample_losses,
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/losses.py in call(self, y_true, y_pred)
219 y_pred, y_true = tf_losses_util.squeeze_or_expand_dimensions(
220 y_pred, y_true)
--> 221 return self.fn(y_true, y_pred, **self._fn_kwargs)
222
223 def get_config(self):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/losses.py in mean_absolute_error(y_true, y_pred)
781 y_pred = ops.convert_to_tensor(y_pred)
782 y_true = math_ops.cast(y_true, y_pred.dtype)
--> 783 return K.mean(math_ops.abs(y_pred - y_true), axis=-1)
784
785
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/ops/math_ops.py in binary_op_wrapper(x, y)
897 with ops.name_scope(None, op_name, [x, y]) as name:
898 if isinstance(x, ops.Tensor) and isinstance(y, ops.Tensor):
--> 899 return func(x, y, name=name)
900 elif not isinstance(y, sparse_tensor.SparseTensor):
901 try:
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py in sub(x, y, name)
11086 # Add nodes to the TensorFlow graph.
11087 _, _, _op = _op_def_lib._apply_op_helper(
> 11088 "Sub", x=x, y=y, name=name)
11089 _result = _op.outputs[:]
11090 _inputs_flat = _op.inputs
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
791 op = g.create_op(op_type_name, inputs, dtypes=None, name=scope,
792 input_types=input_types, attrs=attr_protos,
--> 793 op_def=op_def)
794 return output_structure, op_def.is_stateful, op
795
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in create_op(***failed resolving arguments***)
546 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
547 op_type, inputs, dtypes, input_types, name, attrs, op_def,
--> 548 compute_device)
549
550 def capture(self, tensor, name=None):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3427 input_types=input_types,
3428 original_op=self._default_original_op,
-> 3429 op_def=op_def)
3430 self._create_op_helper(ret, compute_device=compute_device)
3431 return ret
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1771 op_def, inputs, node_def.attr)
1772 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1773 control_input_ops)
1774 # pylint: enable=protected-access
1775
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1611 except errors.InvalidArgumentError as e:
1612 # Convert to ValueError for backwards compatibility.
-> 1613 raise ValueError(str(e))
1614
1615 return c_op
ValueError: Dimensions must be equal, but are 50 and 8 for 'loss/lstm_loss/sub' (op: 'Sub') with input shapes: [?,50], [?,1,8].
数据集在这里可用。
我的问题是：
- Keras LSTM层不是为此设计的吗？也许这只是一个时间序列？
- 如果LSTM是好的，什么是好的修复？
- 如果LSTM不好，如果我想对特征之间的交互进行建模并对所有特征进行预测，我应该使用什么其他方法？
谢谢！
共有1个答案
锺离自明
我在这里找到了一个解决方案（在“多并行系列”下）。我们只需要重塑功能和标签，并在网络中提供信息，它就会起作用！特征应该具有（n_steps，n_features）的形状，而标签应该具有（n_samples，n_features）的形状（如果我们预测1个时间步长）。
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from math import sqrt
from numpy import array, concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from pandas import read_csv, DataFrame, concat
from datetime import datetime
# load data
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)
# manually specify column names
dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'
# mark all NA values with 0
dataset['pollution'].fillna(0, inplace=True)
# drop the first 24 hours
dataset = dataset[24:]
# summarize first 5 rows
print(dataset.head(5))
# save to file
dataset.to_csv('pollution.csv')
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# integer encode direction
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
n_steps = 10
n_features = 8
def split_sequences(sequences, n_steps):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the dataset
if end_ix > len(sequences)-1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
X, y = split_sequences(sequences=scaled, n_steps=n_steps)
print(X.shape, y.shape)
X = X[:1000, :]
y = y[:1000, :]
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', return_sequences=False, input_shape=(n_steps, n_features)))
#model.add(LSTM(100, activation='relu'))
model.add(Dense(n_features))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=100, verbose=1)
# demonstrate prediction
x_input = X[0]
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
类似资料：
-
我正在阅读Keras中关于使用LSTM进行多元时间序列预测的教程https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/#comment-442845 我已经看完了整个教程，遇到了一个如下的问题- 在本教程中，在步骤“t-1”中，列车和测试拆分有8个功能，即“污染”、“露水”、“温度”、
-
问题内容： 我有一个SQLite表，其格式如下： 学生列中有学生姓名，其他列中有描述各个科目的分数的数值。我想为每个学生找到的是他们学科的排名。例如，对于该行， 我想要的输出是 这是每个学生的全部5门科目的排名。输出不必采用我显示的格式。我只需要一个输出，指示每个学生每个学科的排名。如何在SQLite中实现它？ 我发现了RANK和ROW_NUMBER，但不了解如何将它们用于多列。 问题答案： 我的
-
这是我的问题：我有通知列表，希望在不同的时间显示它们，但它们同时显示在一起。 第一次调用updateLabel函数： 私有空updateLabel（ArrayList通知实体）{ 然后创建通知： 私人通知add NotificationEntity（NotificationEntity）{ 然后安排通知： 私有void scheduleNotification（int notificationId
-
介绍 考虑以下示例： 使用运行以升序打印数组中的排序数字： 问题 考虑两个(一般来说，对于< code>N)对应数组< code>a和< code>b的扩展情况 以及“同时”排序所有数组的问题..为了实现这一点，我需要对数组< code>a进行排序，还需要获取排序的索引。对于这个简单的例子，指数由下式给出 有了这些索引，我就可以根据数组 的排序结果打印出排序后的 数组。例如： 将打印排序的数组。。
-
根据教程，我为我的数据集开发了多元输入多步骤LSTM时间序列预测模型(https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/). 然而，我有一个非常奇怪的问题，那就是，当我使用较小的样本（5
-
本文向大家介绍如何在R中对列表的一个或多个子元素进行子集化？，包括了如何在R中对列表的一个或多个子元素进行子集化？的使用技巧和注意事项，需要的朋友参考一下 列表包含不同类型的元素，并且每个元素可以具有不同的元素。为了子集这些子元素，我们可以使用sapply函数，并使用c子集相应子元素的数量。例如，如果我们有一个包含五个元素的列表，并且每个元素都有十个子元素，则可以从子元素中提取1、2、3等元素。