That is the ultimate publish in a fourpart introduction to timeseries forecasting with torch
. These posts have been the story of a quest for multiplestep prediction, and by now, we’ve seen three completely different approaches: forecasting in a loop, incorporating a multilayer perceptron (MLP), and sequencetosequence fashions. Right here’s a fast recap.

As one ought to when one units out for an adventurous journey, we began with an indepth research of the instruments at our disposal: recurrent neural networks (RNNs). We skilled a mannequin to foretell the very subsequent remark in line, after which, considered a intelligent hack: How about we use this for multistep prediction, feeding again particular person predictions in a loop? The outcome , it turned out, was fairly acceptable.

Then, the journey actually began. We constructed our first mannequin “natively” for multistep prediction, relieving the RNN a little bit of its workload and involving a second participant, a tinyish MLP. Now, it was the MLP’s job to mission RNN output to a number of time factors sooner or later. Though outcomes had been fairly passable, we didn’t cease there.

As a substitute, we utilized to numerical time sequence a way generally utilized in pure language processing (NLP): sequencetosequence (seq2seq) prediction. Whereas forecast efficiency was not a lot completely different from the earlier case, we discovered the approach to be extra intuitively interesting, because it displays the causal relationship between successive forecasts.
As we speak we’ll enrich the seq2seq method by including a brand new element: the consideration module. Initially launched round 2014, consideration mechanisms have gained huge traction, a lot so {that a} current paper title begins out “Consideration is Not All You Want”.
The concept is the next.
Within the traditional encoderdecoder setup, the decoder will get “primed” with an encoder abstract only a single time: the time it begins its forecasting loop. From then on, it’s by itself. With consideration, nevertheless, it will get to see the whole sequence of encoder outputs once more each time it forecasts a brand new worth. What’s extra, each time, it will get to zoom in on these outputs that appear related for the present prediction step.
It is a notably helpful technique in translation: In producing the subsequent phrase, a mannequin might want to know what a part of the supply sentence to concentrate on. How a lot the approach helps with numerical sequences, in distinction, will seemingly rely upon the options of the sequence in query.
As earlier than, we work with vic_elec
, however this time, we partly deviate from the way in which we used to make use of it. With the unique, bihourly dataset, coaching the present mannequin takes a very long time, longer than readers will wish to wait when experimenting. So as an alternative, we combination observations by day. In an effort to have sufficient knowledge, we practice on years 2012 and 2013, reserving 2014 for validation in addition to posttraining inspection.
We’ll try to forecast demand as much as fourteen days forward. How lengthy, then, ought to be the enter sequences? It is a matter of experimentation; all of the extra so now that we’re including within the consideration mechanism. (I think that it may not deal with very lengthy sequences so effectively).
Beneath, we go along with fourteen days for enter size, too, however that will not essentially be the very best selection for this sequence.
n_timesteps < 7 * 2
n_forecast < 7 * 2
elec_dataset < dataset(
title = "elec_dataset",
initialize = operate(x, n_timesteps, sample_frac = 1) {
self$n_timesteps < n_timesteps
self$x < torch_tensor((x  train_mean) / train_sd)
n < size(self$x)  self$n_timesteps  1
self$begins < kind(pattern.int(
n = n,
dimension = n * sample_frac
))
},
.getitem = operate(i) {
begin < self$begins[i]
finish < begin + self$n_timesteps  1
lag < 1
record(
x = self$x[start:end],
y = self$x[(start+lag):(end+lag)]$squeeze(2)
)
},
.size = operate() {
size(self$begins)
}
)
batch_size < 32
train_ds < elec_dataset(elec_train, n_timesteps)
train_dl < train_ds %>% dataloader(batch_size = batch_size, shuffle = TRUE)
valid_ds < elec_dataset(elec_valid, n_timesteps)
valid_dl < valid_ds %>% dataloader(batch_size = batch_size)
test_ds < elec_dataset(elec_test, n_timesteps)
test_dl < test_ds %>% dataloader(batch_size = 1)
Mannequinwise, we once more encounter the three modules acquainted from the earlier publish: encoder, decoder, and toplevel seq2seq module. Nevertheless, there’s an extra element: the consideration module, utilized by the decoder to acquire consideration weights.
Encoder
The encoder nonetheless works the identical means. It wraps an RNN, and returns the ultimate state.
encoder_module < nn_module(
initialize = operate(sort, input_size, hidden_size, num_layers = 1, dropout = 0) {
self$sort < sort
self$rnn < if (self$sort == "gru") {
nn_gru(
input_size = input_size,
hidden_size = hidden_size,
num_layers = num_layers,
dropout = dropout,
batch_first = TRUE
)
} else {
nn_lstm(
input_size = input_size,
hidden_size = hidden_size,
num_layers = num_layers,
dropout = dropout,
batch_first = TRUE
)
}
},
ahead = operate(x) {
# return outputs for all timesteps, in addition to lasttimestep states for all layers
x %>% self$rnn()
}
)
Consideration module
In primary seq2seq, at any time when it needed to generate a brand new worth, the decoder took into consideration two issues: its prior state, and the earlier output generated. In an attentionenriched setup, the decoder moreover receives the whole output from the encoder. In deciding what subset of that output ought to matter, it will get assist from a brand new agent, the eye module.
This, then, is the eye module’s raison d’être: Given present decoder state and effectively as full encoder outputs, acquire a weighting of these outputs indicative of how related they’re to what the decoder is at the moment as much as. This process ends in the socalled consideration weights: a normalized rating, for every time step within the encoding, that quantify their respective significance.
Consideration could also be applied in a variety of alternative ways. Right here, we present two implementation choices, one additive, and one multiplicative.
Additive consideration
In additive consideration, encoder outputs and decoder state are generally both added or concatenated (we select to do the latter, under). The ensuing tensor is run by means of a linear layer, and a softmax is utilized for normalization.
attention_module_additive < nn_module(
initialize = operate(hidden_dim, attention_size) {
self$consideration < nn_linear(2 * hidden_dim, attention_size)
},
ahead = operate(state, encoder_outputs) {
# operate argument shapes
# encoder_outputs: (bs, timesteps, hidden_dim)
# state: (1, bs, hidden_dim)
# multiplex state to permit for concatenation (dimensions 1 and a couple of should agree)
seq_len < dim(encoder_outputs)[2]
# ensuing form: (bs, timesteps, hidden_dim)
state_rep < state$permute(c(2, 1, 3))$repeat_interleave(seq_len, 2)
# concatenate alongside function dimension
concat < torch_cat(record(state_rep, encoder_outputs), dim = 3)
# run by means of linear layer with tanh
# ensuing form: (bs, timesteps, attention_size)
scores < self$consideration(concat) %>%
torch_tanh()
# sum over consideration dimension and normalize
# ensuing form: (bs, timesteps)
attention_weights < scores %>%
torch_sum(dim = 3) %>%
nnf_softmax(dim = 2)
# a normalized rating for each supply token
attention_weights
}
)
Multiplicative consideration
In multiplicative consideration, scores are obtained by computing dot merchandise between decoder state and all the encoder outputs. Right here too, a softmax is then used for normalization.
attention_module_multiplicative < nn_module(
initialize = operate() {
NULL
},
ahead = operate(state, encoder_outputs) {
# operate argument shapes
# encoder_outputs: (bs, timesteps, hidden_dim)
# state: (1, bs, hidden_dim)
# enable for matrix multiplication with encoder_outputs
state < state$permute(c(2, 3, 1))
# put together for scaling by variety of options
d < torch_tensor(dim(encoder_outputs)[3], dtype = torch_float())
# scaled dot merchandise between state and outputs
# ensuing form: (bs, timesteps, 1)
scores < torch_bmm(encoder_outputs, state) %>%
torch_div(torch_sqrt(d))
# normalize
# ensuing form: (bs, timesteps)
attention_weights < scores$squeeze(3) %>%
nnf_softmax(dim = 2)
# a normalized rating for each supply token
attention_weights
}
)
Decoder
As soon as consideration weights have been computed, their precise software is dealt with by the decoder. Concretely, the tactic in query, weighted_encoder_outputs()
, computes a product of weights and encoder outputs, ensuring that every output can have acceptable impression.
The remainder of the motion then occurs in ahead()
. A concatenation of weighted encoder outputs (typically known as “context”) and present enter is run by means of an RNN. Then, an ensemble of RNN output, context, and enter is handed to an MLP. Lastly, each RNN state and present prediction are returned.
decoder_module < nn_module(
initialize = operate(sort, input_size, hidden_size, attention_type, attention_size = 8, num_layers = 1) {
self$sort < sort
self$rnn < if (self$sort == "gru") {
nn_gru(
input_size = input_size,
hidden_size = hidden_size,
num_layers = num_layers,
batch_first = TRUE
)
} else {
nn_lstm(
input_size = input_size,
hidden_size = hidden_size,
num_layers = num_layers,
batch_first = TRUE
)
}
self$linear < nn_linear(2 * hidden_size + 1, 1)
self$consideration < if (attention_type == "multiplicative") attention_module_multiplicative()
else attention_module_additive(hidden_size, attention_size)
},
weighted_encoder_outputs = operate(state, encoder_outputs) {
# encoder_outputs is (bs, timesteps, hidden_dim)
# state is (1, bs, hidden_dim)
# ensuing form: (bs * timesteps)
attention_weights < self$consideration(state, encoder_outputs)
# ensuing form: (bs, 1, seq_len)
attention_weights < attention_weights$unsqueeze(2)
# ensuing form: (bs, 1, hidden_size)
weighted_encoder_outputs < torch_bmm(attention_weights, encoder_outputs)
weighted_encoder_outputs
},
ahead = operate(x, state, encoder_outputs) {
# encoder_outputs is (bs, timesteps, hidden_dim)
# state is (1, bs, hidden_dim)
# ensuing form: (bs, 1, hidden_size)
context < self$weighted_encoder_outputs(state, encoder_outputs)
# concatenate enter and context
# NOTE: this repeating is completed to compensate for the absence of an embedding module
# that, in NLP, would give x a better proportion within the concatenation
x_rep < x$repeat_interleave(dim(context)[3], 3)
rnn_input < torch_cat(record(x_rep, context), dim = 3)
# ensuing shapes: (bs, 1, hidden_size) and (1, bs, hidden_size)
rnn_out < self$rnn(rnn_input, state)
rnn_output < rnn_out[[1]]
next_hidden < rnn_out[[2]]
mlp_input < torch_cat(record(rnn_output$squeeze(2), context$squeeze(2), x$squeeze(2)), dim = 2)
output < self$linear(mlp_input)
# shapes: (bs, 1) and (1, bs, hidden_size)
record(output, next_hidden)
}
)
seq2seq
module
The seq2seq
module is principally unchanged (aside from the truth that now, it permits for consideration module configuration). For an in depth rationalization of what occurs right here, please seek the advice of the earlier publish.
seq2seq_module < nn_module(
initialize = operate(sort, input_size, hidden_size, attention_type, attention_size, n_forecast,
num_layers = 1, encoder_dropout = 0) {
self$encoder < encoder_module(sort = sort, input_size = input_size, hidden_size = hidden_size,
num_layers, encoder_dropout)
self$decoder < decoder_module(sort = sort, input_size = 2 * hidden_size, hidden_size = hidden_size,
attention_type = attention_type, attention_size = attention_size, num_layers)
self$n_forecast < n_forecast
},
ahead = operate(x, y, teacher_forcing_ratio) {
outputs < torch_zeros(dim(x)[1], self$n_forecast)
encoded < self$encoder(x)
encoder_outputs < encoded[[1]]
hidden < encoded[[2]]
# record of (batch_size, 1), (1, batch_size, hidden_size)
out < self$decoder(x[ , n_timesteps, , drop = FALSE], hidden, encoder_outputs)
# (batch_size, 1)
pred < out[[1]]
# (1, batch_size, hidden_size)
state < out[[2]]
outputs[ , 1] < pred$squeeze(2)
for (t in 2:self$n_forecast) {
teacher_forcing < runif(1) < teacher_forcing_ratio
enter < if (teacher_forcing == TRUE) y[ , t  1, drop = FALSE] else pred
enter < enter$unsqueeze(3)
out < self$decoder(enter, state, encoder_outputs)
pred < out[[1]]
state < out[[2]]
outputs[ , t] < pred$squeeze(2)
}
outputs
}
)
When instantiating the toplevel mannequin, we now have an extra selection: that between additive and multiplicative consideration. Within the “accuracy” sense of efficiency, my checks didn’t present any variations. Nevertheless, the multiplicative variant is loads sooner.
web < seq2seq_module("gru", input_size = 1, hidden_size = 32, attention_type = "multiplicative",
attention_size = 8, n_forecast = n_forecast)
Similar to final time, in mannequin coaching, we get to decide on the diploma of instructor forcing. Beneath, we go along with a fraction of 0.0, that’s, no forcing in any respect.
optimizer < optim_adam(web$parameters, lr = 0.001)
num_epochs < 1000
train_batch < operate(b, teacher_forcing_ratio) {
optimizer$zero_grad()
output < web(b$x, b$y, teacher_forcing_ratio)
goal < b$y
loss < nnf_mse_loss(output, goal[ , 1:(dim(output)[2])])
loss$backward()
optimizer$step()
loss$merchandise()
}
valid_batch < operate(b, teacher_forcing_ratio = 0) {
output < web(b$x, b$y, teacher_forcing_ratio)
goal < b$y
loss < nnf_mse_loss(output, goal[ , 1:(dim(output)[2])])
loss$merchandise()
}
for (epoch in 1:num_epochs) {
web$practice()
train_loss < c()
coro::loop(for (b in train_dl) {
loss <train_batch(b, teacher_forcing_ratio = 0.0)
train_loss < c(train_loss, loss)
})
cat(sprintf("nEpoch %d, coaching: loss: %3.5f n", epoch, imply(train_loss)))
web$eval()
valid_loss < c()
coro::loop(for (b in valid_dl) {
loss < valid_batch(b)
valid_loss < c(valid_loss, loss)
})
cat(sprintf("nEpoch %d, validation: loss: %3.5f n", epoch, imply(valid_loss)))
}
# Epoch 1, coaching: loss: 0.83752
# Epoch 1, validation: loss: 0.83167
# Epoch 2, coaching: loss: 0.72803
# Epoch 2, validation: loss: 0.80804
# ...
# ...
# Epoch 99, coaching: loss: 0.10385
# Epoch 99, validation: loss: 0.21259
# Epoch 100, coaching: loss: 0.10396
# Epoch 100, validation: loss: 0.20975
For visible inspection, we decide a couple of forecasts from the take a look at set.
web$eval()
test_preds < vector(mode = "record", size = size(test_dl))
i < 1
vic_elec_test < vic_elec_daily %>%
filter(12 months(Date) == 2014, month(Date) %in% 1:4)
coro::loop(for (b in test_dl) {
output < web(b$x, b$y, teacher_forcing_ratio = 0)
preds < as.numeric(output)
test_preds[[i]] < preds
i << i + 1
})
test_pred1 < test_preds[[1]]
test_pred1 < c(rep(NA, n_timesteps), test_pred1, rep(NA, nrow(vic_elec_test)  n_timesteps  n_forecast))
test_pred2 < test_preds[[21]]
test_pred2 < c(rep(NA, n_timesteps + 20), test_pred2, rep(NA, nrow(vic_elec_test)  20  n_timesteps  n_forecast))
test_pred3 < test_preds[[41]]
test_pred3 < c(rep(NA, n_timesteps + 40), test_pred3, rep(NA, nrow(vic_elec_test)  40  n_timesteps  n_forecast))
test_pred4 < test_preds[[61]]
test_pred4 < c(rep(NA, n_timesteps + 60), test_pred4, rep(NA, nrow(vic_elec_test)  60  n_timesteps  n_forecast))
test_pred5 < test_preds[[81]]
test_pred5 < c(rep(NA, n_timesteps + 80), test_pred5, rep(NA, nrow(vic_elec_test)  80  n_timesteps  n_forecast))
preds_ts < vic_elec_test %>%
choose(Demand, Date) %>%
add_column(
ex_1 = test_pred1 * train_sd + train_mean,
ex_2 = test_pred2 * train_sd + train_mean,
ex_3 = test_pred3 * train_sd + train_mean,
ex_4 = test_pred4 * train_sd + train_mean,
ex_5 = test_pred5 * train_sd + train_mean) %>%
pivot_longer(Date) %>%
update_tsibble(key = title)
preds_ts %>%
autoplot() +
scale_color_hue(h = c(80, 300), l = 70) +
theme_minimal()
We will’t instantly examine efficiency right here to that of earlier fashions in our sequence, as we’ve pragmatically redefined the duty. The primary purpose, nevertheless, has been to introduce the idea of consideration. Particularly, learn how to manually implement the approach – one thing that, when you’ve understood the idea, you might by no means must do in observe. As a substitute, you’ll seemingly make use of present instruments that include torch
(multihead consideration and transformer modules), instruments we could introduce in a future “season” of this sequence.
Thanks for studying!
Photograph by David Clode on Unsplash