CN109002917A - Total output of grain multidimensional time-series prediction technique based on LSTM neural network - Google Patents
Total output of grain multidimensional time-series prediction technique based on LSTM neural network Download PDFInfo
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Abstract
The present invention discloses a kind of total output of grain multidimensional time-series prediction technique based on LSTM neural network, based on LSTM neural network, multiple dimension variables using the yield data of a variety of agricultural product as LSTM neural network input data, the multidimensional time-series prediction model based on LSTM neural network is established, because it is contemplated that the yield datas of a variety of agricultural product, and using the LSTM being superimposed come processing sequence data information, avoid gradient disappearance problem, prediction effect is good, and precision is high, and applicability is good.
Description
Technical field
The present invention relates to a kind of total output of grain prediction techniques, are a kind of grain based on LSTM neural network specifically
Total output multidimensional time-series prediction technique is eaten, Grain Crop Yield Prediction field is belonged to.
Background technique
At present for the research in Grain Crop Yield Prediction field, formd a variety of prediction techniques, as exponential smoothing model,
Grey method, ARIMA model, remote sensing technology prediction model, statistical dynamics growth model, Meteorological Output prediction model, mind
Through network model etc..Through investigating, these methods only use the historical data of grain yield to the grain in following a period of time mostly
Food yield is predicted, and the total output of grain is often influenced by other agricultural product, and such as cultivated area, certain situation is sowed
The farmland area just opposite reduction that other crops are planted when the farmland area increase of corn is planted, corresponding crop yield is with regard to phase
To reduction.Therefore, total output of grain is predicted using a variety of agricultural product as multiple dimensions, accuracy is relatively high.
With the rapid development of depth learning technology, Recognition with Recurrent Neural Network (RNN) has been obtained extensively in multiple fields
Application, and achieve good effect.And Recognition with Recurrent Neural Network is most common tool in processing time series problem, because
There is memory function for it, the result before can remembeing several times.But its memory function is limited, can not remember for an infinity
Before as a result, can not solve the problems, such as long-distance dependence in turn.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of total output of grain multi-dimensional times based on LSTM neural network
Sequence prediction method establishes the multidimensional time-series prediction model based on LSTM neural network, using the yield of a variety of agricultural product
Multiple dimension variables of the data as input data, prediction effect is good, and precision is high.
In order to solve the technical problem, the technical solution adopted by the present invention is that: the grain based on LSTM neural network is total
Yield multidimensional time-series prediction technique, comprising the following steps: S01), input data is pre-processed, when taking over specific
Between training set of a variety of agricultural output data as LSTM neural network in section, before being trained to LSTM neural network,
The pretreatment that the data of training set are normalized first, if training set is matrix X,
By becoming square after normalized
Battle array:
WhereinI=1,2 ..., n, j=1,2 ..., 24, here
For matrix XRThe minimum value of jth column variable,For matrix XRThe maximum value of jth column variable,For matrix XRJth column variable it is very poor, by transformation after, gained agricultural output numerical value is in 0-
Between 1, normalized is completed；
S02), LSTM neural network framework is designed, LSTM neural network framework includes input layer, LSTM layers, Dropout
Pretreated data are passed through in layer, LSTM layers, Dropout layers, Dense layers and output layer, input layer input, then in turn through
LSTM layers, Dropout layers, LSTM layers, Dropout layers, Dense layers of interative computation, finally export prediction result；Wherein LSTM
Layer calculates input data using LSTM function；Dropout layers train the model over-fitting come in order to prevent, it is in mould
Temporarily disconnect the neuron of specified quantity in type training process when undated parameter at random；Dense layers are full articulamentums, and operation is
Output=activation (dot (input, weight)+bias), wherein activation is activation primitive, and dot is matrix
Multiplication operation, input are that Dense layers of input and the data of input layer successively pass through LSTM layers, Dropout layers, LSTM
The output valve that layer, Dropout layers of operation obtain, weight are weight matrix, and bias is bias, and output is exactly last defeated
Prediction result out.
Further, the LSTM layers of calculating using cumulative linear forms come processing sequence data information, inside LSTM layers
Process is；
ft=σ (Wf·[ht-1,Xt]+bf) (1),
it=σ (Wi·[ht-1,Xt]+bi) (2),
ot=σ (Wo·[ht-1,Xt]+bo) (5),
ht=ot·tanh(ct) (6),
Wherein, ftIt indicates to forget door, σ indicates sigmoid function, WfIndicate the weight matrix of forgetting door, ht-1Indicate upper one
The output at moment, XtIndicate the input data at current time, [ht-1,Xt] indicate ht-1、XtIt is spliced into the horizontal direction one long
Vector, bfIndicate the bias of forgetting door, itIndicate input gate, WiIndicate the weight matrix of input gate, biIndicate the inclined of input gate
Value is set,Indicate location mode currently entered, WcIndicate the weight matrix of location mode currently entered, bcIndicate current defeated
The bias of the location mode entered, ctIndicate the location mode at current time, it is by forgetting door ftMultiplied by the unit of last moment
State ct-1, add input gate itMultiplied by location mode currently enteredotIndicate out gate, WoIndicate the weight of out gate
Matrix, boIndicate the bias of out gate, htIndicate final output, it is by out gate otMultiplied by current time location mode ct
Tanh functional value.
Further, activation primitive is ReLU function.
Further, LSTM layers of input data is 3D that shape is (samples, timesteps, input_dim)
Amount, samples indicate that sample size, timesteps indicate the length of time window, and input_dim indicates the dimension of input data
Degree.
Further, when taking training set, using the time as unit, a variety of agricultural output data conducts for the M of taking over
The training set of LSTM neural network, M are positive integer.
Beneficial effects of the present invention: the present invention is based on LSTM neural networks, using the yield data conduct of a variety of agricultural product
Multiple dimension variables of LSTM neural network input data establish the multidimensional time-series based on LSTM neural network and predict mould
Type, because it is contemplated that the yield datas of a variety of agricultural product, and ladder is avoided come processing sequence data information using the LSTM of superposition
Disappearance problem is spent, prediction effect is good, and precision is high, and applicability is good.
Detailed description of the invention
Fig. 1 is the schematic diagram of LSTM door；
Fig. 2 is the schematic diagram of LSTM neural network framework；
Fig. 3 is the schematic diagram for testing test penalty values and training penalty values in 1, and wherein curve A indicates training penalty values, bent
Line B indicates test penalty values；
Fig. 4 is the schematic diagram for testing grain yield truthful data and prediction data comparing result in 1；
Fig. 5 is the schematic diagram for testing test penalty values and training penalty values in 2, and wherein curve C indicates training penalty values, bent
Line D indicates test penalty values；
Fig. 6 is the schematic diagram for testing grain yield truthful data and prediction data comparing result in 2；
Fig. 7 is the schematic diagram for testing test penalty values and training penalty values in 3, and wherein curve E indicates training penalty values, bent
Line F indicates test penalty values；
Fig. 8 is the schematic diagram for testing grain yield truthful data and prediction data comparing result in 3.
Specific embodiment
The present invention is further illustrated in the following with reference to the drawings and specific embodiments.
The present embodiment discloses a kind of total output of grain multidimensional time-series prediction technique based on LSTM neural network, including
Following steps:
S01), the pretreatment of input data
In engineering practice, the data that we obtain are often imperfect, Noise, inconsistent, duplicate, and numerous
Deep learning algorithm in the quality of input data determine the quality of model in training process again, therefore we are using data
Before being trained to model, data need to be pre-processed.The process of data prediction is not usually fixation, it is usually because appointing
Business and the difference of data set attribute and it is different.
In deep learning field, before being trained using training the set pair analysis model, it usually needs data are normalized pre-
Processing operation accelerates convergence rate when model training primarily to data are limited in [0,1] range, is convenient for data
Processing.Normalized operation has equally been carried out to training set according to the feature of training set in the present embodiment.Normalized
There are many mode, the normalized calculation formula that we use in the present embodiment is as follows.
If data set is matrix X, then
By becoming matrix after normalized:
WhereinI=1,2 ..., n, j=1,2 ..., 24, here
For matrix XRThe minimum value of jth column variable,For matrix XRThe maximum value of jth column variable,For matrix XRJth column variable it is very poor, by transformation after, gained agricultural output numerical value is in 0-
Between 1, normalized is completed；
S02), LSTM neural network framework is designed
In the training process of deep learning model, the design of the network architecture is played a crucial role, it is determined
The quality of model directly affects the prediction result of data.The quick of deep learning is realized in the present embodiment using keras frame
Prototype because keras provides multiple network frame for selection by the user, user can based on existing frame, according to
The new web original of the Demand Design of oneself；Meanwhile constructing that code required when various networks is fewer, desin speed is fast.
The LSTM network architecture of stacking is selected to predict total output of grain in the present embodiment, LSTM neural network framework
As shown in Fig. 2, including input layer, LSTM layers, Dropout layers, LSTM layers, Dropout layers, Dense layers and output layer, input layer
Pretreated data are passed through in input, then in turn through LSTM layers, Dropout layers, LSTM layers, Dropout layers, Dense layers
Interative computation finally exports prediction result；Wherein input data is calculated using LSTM function for LSTM layers；Dropout layers
The model over-fitting come is trained in order to prevent, it temporarily disconnects specified quantity when undated parameter during model training at random
Neuron；Dense layers are full articulamentums, operation be output=activation (dot (input, weight)+
Bias), wherein activation is activation primitive, and dot is matrix multiple operation, and input is Dense layers of input and defeated
Enter data and successively pass through LSTM layers, Dropout layers, LSTM layers, the obtained output valve of Dropout layers of operation, weight is power
Weight matrix, bias is bias, and output is exactly the prediction result finally exported.Dot (input, weight) indicates input number
It is multiplied according to weight matrix.
In the present embodiment, LSTM layers of input data is the 3D that shape is (samples, timesteps, input_dim)
Tensor, samples indicate that sample size, timesteps indicate the length of time window, and input_dim indicates input data
Dimension.Activation primitive is ReLU function.
In the present embodiment, the LSTM network architecture uses two LSTM and is overlapped, including two LSTM layers, each LSTM
Layer is followed by one Dropout layers, and each Dropout layers respectively updates the LSTM layer of the front during model training
The neuron for temporarily disconnecting specified quantity when parameter at random trains the model over-fitting come for preventing.
As shown in Figure 1, being the schematic diagram of LSTM door, X indicates input data, and h indicates output data, and C indicates unit
The value of state.The content of location mode C is mainly controlled by forgeing door and input gate.The location mode content at current timeIt is by part last moment location mode content Ct-1The input X current with parttComposition includes last moment location mode
The number of content is controlled by forgetting door, how much is controlled by input gate comprising currently entered.htIndicate current output valve,
It is a part of location mode content, its value is controlled by out gate.LSTM layers of calculating process are as follows:
ft=σ (Wf·[ht-1,Xt]+bf) (1),
it=σ (Wi·[ht-1,Xt]+bi) (2),
ot=σ (Wo·[ht-1,Xt]+bo) (5),
ht=ot·tanh(ct) (6),
Wherein, ftIt indicates to forget door, σ indicates sigmoid function, WfIndicate the weight matrix of forgetting door, ht-1Indicate upper one
The output at moment, XtIndicate the input data at current time, [ht-1,Xt] indicate ht-1、XtIt is spliced into the horizontal direction one long
Vector, bfIndicate the bias of forgetting door, itIndicate input gate, WiIndicate the weight matrix of input gate, biIndicate the inclined of input gate
Value is set,Indicate location mode currently entered, WcIndicate the weight matrix of location mode currently entered, bcIndicate current defeated
The bias of the location mode entered, ctIndicate the location mode at current time, it is by forgetting door ftMultiplied by the unit of last moment
State ct-1, add input gate itMultiplied by location mode currently enteredotIndicate out gate, WoIndicate the weight of out gate
Matrix, boIndicate the bias of out gate, htIndicate final output, it is by out gate otMultiplied by current time cell-like
State ctTanh functional value.
By the training calculating process of LSTM it is found that LSTM layers using cumulative linear forms come processing sequence data information,
Gradient disappearance problem is avoided, can also be acquired apart from longer contextual information.
In the present embodiment, when taking training set, using the time as unit, the plurality of cereals yield data conduct for the M of taking over
The training set of LSTM neural network, M are positive integer.
The prediction effect of this prediction technique, accuracy are analyzed below with reference to specific experiment.
Experiment 1 carries out multidimensional time-series prediction based on LSTM neural network
In this experiment, total output of grain is predicted using multidimensional variable based on LSTM neural network, uses soybean, small
Wheat, corn, paddy, grain multiple dimension variables of the yield data as input data, according to past historical data to not
The total output of grain come is predicted.Chosen in this experiment -1999 years 1949 agricultural product (soybean, wheat, corn, paddy,
Grain) yield data is as training dataset, and the agricultural output data of selection 2000 to 2005 are as test data set.
Partial data format is as shown in table 1.
1 soybean of table, wheat, corn, paddy, grain yield partial data table
Performance metric is carried out to model as loss function using mean square deviation (MSE) in this experiment.When Fig. 3 shows multidimensional
Between test and training loss curve graph during sequence LSTM model training.
Fig. 3 shows the output penalty values of model test data and training data at the end of each iteration.It is instructed in model
After white silk, which is 0.00914 to final mean square deviation (MSE) value of test data set, achieves higher accuracy.
Test data using trained model to 2000 to 2005 is predicted, and is returned to predicted value is counter
One changes, and the prediction data after reduction is illustrated in fig. 4 shown below with truthful data comparing result.
As shown in Figure 4, the multidimensional time-series prediction model based on LSTM neural network has preferably test data
Fitting effect, prediction data and truthful data and its prediction error are as shown in table 2.
- 2005 years 2 2000 years grain yield truthful datas of table and prediction data compare
| Time | True value | Predicted value | Error rate |
| 2000 | 3837.7 | 3908.106 | 1.835% |
| 2001 | 3720.6 | 3657.555 | 1.694% |
| 2002 | 3292.7 | 3378.649 | 2.610% |
| 2003 | 3435.5 | 3402.470 | 0.961% |
| 2004 | 3516.7 | 3463.012 | 1.527% |
| 2005 | 3917.4 | 3985.137 | 1.729% |
Multidimensional time-series error prediction model very little based on LSTM neural network as can be seen from Table 2, it is most of
The error of data has very high predictablity rate less than 2%.
Experiment 2, the One-dimension Time Series prediction based on LSTM neural network
One-dimension Time Series prediction based on LSTM neural network, which refers to, utilizes grain yield history based on LSTM neural network
Data predict the grain yield of next year.It and multidimensional time-series predict that maximum is not both to only use total grain output
Data (one-dimensional data) is measured as trained and test data.- 1999 years 1949 total output of grain data are chosen in experiment to make
For training dataset, 2000 to 2005 total output of grain data are chosen as test data set.Using mean square deviation (MSE)
Performance metric is carried out to model as loss function.Test, training loss during LSTM One-dimension Time Series model training
Value figure is as shown in Figure 5.
Fig. 5 shows the penalty values of training data and test data in 100 iterative process.After the completion of model training, make
Test data with trained model to 2000 to 2005 is predicted, and carries out renormalization, reduction to predicted value
Prediction data afterwards and truthful data comparing result are as shown in Figure 6.Prediction data and truthful data and its prediction error such as 3 institute of table
Show.
- 2005 years 3 2000 years grain yield truthful datas of table and prediction data compare
Test 3, the Grain Crop Yield Prediction based on BP neural network
BP neural network is a kind of network that error progress model training is calculated according to back-propagation algorithm.It is mainly pair
The brain of the mankind is simulated, and when model training need to only give input data and output data, and the mapping without describing variable is closed
System, it can be automatically stored and learn inputoutput data, have very strong nonlinear system simulation ability.It is used herein to include
One input layer, three hidden layers, an output layer BP neural network structure total output of grain is predicted.It is selected in experiment
It takes -1999 years 1949 grain yield data as training dataset, chooses 2000 to 2005 grain yield data
As test data set.Performance metric is carried out to model as loss function using mean square deviation (MSE).Fig. 7 shows BP nerve
Test and training penalty values curve graph in network model training process.
Test data using trained model to 2000 to 2005 predicts, truthful data and prediction data
Comparing result is as shown in Figure 8.
Prediction data and truthful data and its prediction error are as shown in table 4.
- 2005 years 4 2000 years grain yield truthful datas of table and prediction data compare
| Time | True value | Predicted value | Error rate |
| 2000 | 3837.7 | 4060.180 | 5.797% |
| 2001 | 3720.6 | 3905.564 | 4.971% |
| 2002 | 3292.7 | 3445.498 | 4.641% |
| 2003 | 3435.5 | 3556.124 | 3.511% |
| 2004 | 3516.7 | 3476.882 | 1.132% |
| 2005 | 3917.4 | 4112.364 | 4.977% |
The multidimensional time-series prediction model based on LSTM neural network has above been used respectively, is based on LSTM nerve net
One-dimension Time Series prediction model, the BP neural network model of network predict -2005 years 2000 total output of grains.
It can be seen that the prediction error of the multidimensional time-series prediction model based on LSTM neural network is minimum in experiment, BP neural network
The prediction error of model is maximum.
In order to more intuitively be compared to three kinds of methods, table 5 calculates three kinds of sides using mean percent error (MAPE)
The prediction error of method.Mean percent error is as shown in the table.
5 three kinds of prediction technique MAPE value comparisons of table
| Method | Mean percent error |
| Multidimensional time-series prediction based on LSTM neural network | 1.726% |
| One-dimension Time Series prediction based on LSTM neural network | 3.973% |
| Grain Crop Yield Prediction based on BP neural network | 4.172% |
The above results show that the mean percent error of the multidimensional time-series prediction technique based on LSTM neural network is far small
In One-dimension Time Series prediction technique and Multi-layer BP Neural Network prediction technique, good prediction effect is reached.
Described above is only basic principle and preferred embodiment of the invention, and those skilled in the art do according to the present invention
Improvement and replacement out, belong to the scope of protection of the present invention.
Claims (5)
1. the total output of grain multidimensional time-series prediction technique based on LSTM neural network, it is characterised in that: including following step
It is rapid: S01), input data is pre-processed, a variety of agricultural output data in the special time period of taking over as LSTM mind
Training set through network, before being trained to LSTM neural network, the pretreatment that the data of training set are normalized first,
If training set is matrix X,
By becoming matrix after normalized:
WhereinI=1,2 ..., n, j=1,2 ..., 24, here
For matrix XRThe minimum value of jth column variable,For matrix XRThe maximum value of jth column variable,For matrix XRJth column variable it is very poor, by transformation after, gained agricultural output numerical value is in 0-
Between 1, normalized is completed；
S02), design LSTM neural network framework, LSTM neural network framework include input layer, LSTM layers, Dropout layers,
LSTM layers, Dropout layers, Dense layers and output layer, pretreated data are passed through in input layer input, then in turn through LSTM
Layer, Dropout layers, LSTM layers, Dropout layers, Dense layers of interative computation, finally export prediction result；Wherein make for LSTM layers
Input data is calculated with LSTM function；Dropout layers train the model over-fitting come in order to prevent, it is instructed in model
Temporarily disconnect the neuron of specified quantity during white silk when undated parameter at random；Dense layers are full articulamentums, and operation is
Output=activation (dot (input, weight)+bias), wherein activation is activation primitive, and dot is matrix
Multiplication operation function, input is Dense layers of input and the data of input layer successively pass through LSTM layers, Dropout layers,
LSTM layers, the obtained output valve of Dropout layers of operation, weight is weight matrix, and bias is bias, and output is exactly most
The prediction result exported afterwards.
2. the total output of grain multidimensional time-series prediction technique according to claim 1 based on LSTM neural network,
Be characterized in that: LSTM layers using cumulative linear forms come processing sequence data information, the calculating process inside LSTM layers is；
ft=σ (Wf·[ht-1,Xt]+bf) (1),
it=σ (Wi·[ht-1,Xt]+bi) (2),
ot=σ (Wo·[ht-1,Xt]+bo) (5),
ht=ot·tanh(ct) (6),
Wherein, ftIt indicates to forget door, σ indicates sigmoid function, WfIndicate the weight matrix of forgetting door, ht-1Indicate last moment
Output, XtIndicate the input data at current time, [ht-1,Xt] indicate ht-1、XtBe spliced into the horizontal direction it is one long to
Amount, bfIndicate the bias of forgetting door, itIndicate input gate, WiIndicate the weight matrix of input gate, biIndicate the biasing of input gate
Value,Indicate location mode currently entered, WcIndicate the weight matrix of location mode currently entered, bcIndicate current input
Location mode bias, ctIndicate the location mode at current time, it is by forgetting door ftMultiplied by the cell-like of last moment
State ct-1, add input gate itMultiplied by location mode currently enteredotIndicate out gate, WoIndicate the weight square of out gate
Battle array, boIndicate the bias of out gate, htIndicate final output, it is by out gate otMultiplied by current time location mode ct's
Tanh functional value.
3. the total output of grain multidimensional time-series prediction technique according to claim 1 based on LSTM neural network,
Be characterized in that: activation primitive is ReLU function.
4. the total output of grain multidimensional time-series prediction technique according to claim 1 based on LSTM neural network,
Be characterized in that: LSTM layers of input data is the 3D tensor that shape is (samples, timesteps, input_dim),
Samples indicates that sample size, timesteps indicate the length of time window, and input_dim indicates the dimension of input data.
5. the total output of grain multidimensional time-series prediction technique according to claim 1 based on LSTM neural network,
Be characterized in that: when taking training set, using the time as unit, a variety of agricultural output data for the M of taking over are as LSTM nerve net
The training set of network, M are positive integer.
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