Monthly International Maize Price Data
Description
Monthly international Maize price (Dollor per million ton) from January 2010 to June 2020.
Usage
data("Data_Maize")Format
A time series data with 126 observations.
pricea time series
Details
Dataset contains 126 observations of monthly international Maize price (Dollor per million ton). It is obtained from World Bank "Pink sheet".
Source
https://www.worldbank.org/en/research/commodity-markets
References
https://www.worldbank.org/en/research/commodity-markets
Examples
data(Data_Maize)
Gated Recurrent Unit Model
Description
The GRU function computes forecasted value with different forecasting evaluation criteria for gated recurrent unit model.
Usage
GRU_ts(xt, xtlag = 4, uGRU = 2, Drate = 0, nEpochs = 10,
Loss = "mse", AccMetrics = "mae",ActFn = "tanh",
Split = 0.8, Valid = 0.1)Arguments
xt
Input univariate time series (ts) data.
xtlag
Lag of time series data.
uGRU
Number of unit in GRU layer.
Drate
Dropout rate.
nEpochs
Number of epochs.
Loss
Loss function.
AccMetrics
Metrics.
ActFn
Activation function.
Split
Index of the split point and separates the data into the training and testing datasets.
Valid
Validation set.
Details
The gated recurrent unit (GRU) was introduced by Cho et al.(2014). A GRU is part of a specific model of recurrent neural network that intends to use connections through a sequence of nodes to perform machine learning tasks associated with memory and clustering. Its internal structure is simpler and, therefore, it is also easier to train, as less calculation is required to upgrade the internal states. The update port controls the extent to which the state information from the previous moment is retained in the current state, while the reset port determines whether the current state should be combined with the previous information. Gated recurrent units help to adjust neural network input weights to solve the vanishing gradient problem that is a common issue with recurrent neural networks.
Value
TrainFittedValue
Training Fitted value for given time series data.
TestPredictedValue
Final forecasted value of the GRU model.
fcast_criteria
Different Forecasting evaluation criteria for GRU model.
References
Cho, K., Van Merriënboer, B., Bahdanau, D. and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259.
See Also
LSTM, RNN
Examples
data("Data_Maize")
GRU_ts(Data_Maize)
Long- Short Term Memory Model
Description
The LSTM function computes forecasted value with different forecasting evaluation criteria for long- short term memory model.
Usage
LSTM_ts(xt, xtlag = 4, uLSTM = 2, Drate = 0, nEpochs = 10,
Loss = "mse", AccMetrics = "mae",ActFn = "tanh",
Split = 0.8, Valid = 0.1)Arguments
xt
Input univariate time series (ts) data.
xtlag
Lag of time series data.
uLSTM
Number of unit in LSTM layer.
Drate
Dropout rate.
nEpochs
Number of epochs.
Loss
Loss function.
AccMetrics
Metrics.
ActFn
Activation function.
Split
Index of the split point and separates the data into the training and testing datasets.
Valid
Validation set.
Details
Long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997) based RNN is designed to overcome the vanishing gradients problem while dealing with long term dependencies. In contrast to standard RNN, LSTM has this peculiar and unique inbuilt ability by maintaining a memory cell to determine which unimportant features should be forgotten and which important features should be remembered during the learning process (Jaiswal et al., 2022). An LSTM model analyses and captures both short-term and long-term temporal dependencies of a complex time series effectively due to its architecture of recurrent neural network and the memory function used in the hidden nodes.
Value
TrainFittedValue
Training Fitted value for given time series data.
TestPredictedValue
Final forecasted value of the LSTM model.
fcast_criteria
Different Forecasting evaluation criteria for LSTM model.
References
Cho, K., Van Merriënboer, B., Bahdanau, D. and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259.
See Also
GRU, RNN
Examples
data("Data_Maize")
LSTM_ts(Data_Maize)
Recurrent neural network Model
Description
The RNN function computes forecasted value with different forecasting evaluation criteria for recurrent neural network model.
Usage
RNN_ts(xt, xtlag = 4, uRNN = 2, Drate = 0, nEpochs = 10,
Loss = "mse", AccMetrics = "mae",ActFn = "tanh",
Split = 0.8, Valid = 0.1)Arguments
xt
Input univariate time series (ts) data.
xtlag
Lag of time series data.
uRNN
Number of unit in RNN layer.
Drate
Dropout rate.
nEpochs
Number of epochs.
Loss
Loss function.
AccMetrics
Metrics.
ActFn
Activation function.
Split
Index of the split point and separates the data into the training and testing datasets.
Valid
Validation set.
Details
Recurrent neural networks (RNNs) (Rumelhart 1986) add the explicit handling of order between observations when learning a mapping function from inputs to outputs. RNNs actually process single elements of any input sequence at a particular time, and maintain a ‘state vector’ in their hidden units. Nevertheless, when the interval of data dependencies increases, the standard RNNs tend to suffer increasingly heavily from the problem of either vanishing gradient or exploding gradient (Bengio et al. 1994; Lin et al. 1996).
Value
TrainFittedValue
Training Fitted value for given time series data.
TestPredictedValue
Final forecasted value of the RNN model.
fcast_criteria
Different Forecasting evaluation criteria for RNN model.
References
Bengio et al. 1994; Lin Sagheer A, Kotb M (2019) Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323: 203–213.
Rumelhart DE (1986) Learning internal representations by error propagation. In: Parallel distributed processing: Explorations in the microstructure of cognition. pp 318–362.
Jha, G. K. and Sinha, K. (2014). Time-delay neural networks for time series prediction: An application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 24(3–4), 563–571. Jaiswal, R., Jha, G. K., Kumar, R. R. and Choudhary, K. (2022). Deep long short-term memory based model for agricultural price forecasting. Neural Computing and Applications, 34(6), 4661–4676.
See Also
LSTM, GRU
Examples
data("Data_Maize")
RNN_ts(Data_Maize)