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How it works

TL;DR this is seq2seq model with some additions to utilize year-to-year and quarter-to-quarter seasonality in data.

There are two main information sources for prediction:

  1. Local features. If we see a trend, we expect that it will continue (AutoRegressive model), if we see a traffic spike, it will gradually decay (Moving Average model), if wee see more traffic on holidays, we expect to have more traffic on holidays in the future (seasonal model).
  2. Global features. If we look to autocorrelation plot, we'll notice strong year-to-year autocorrelation and some quarter-to-quarter autocorrelation.


The good model should use both global and local features, combining them in a intelligent way.

I decided to use RNN seq2seq model for prediction, because:

  1. RNN can be thought as a natural extension of well-studied ARIMA models, but much more flexible and expressive.
  2. RNN is non-parametric, that's greatly simplifies learning. Imagine working with different ARIMA parameters for 145K timeseries.
  3. Any exogenous feature (numerical or categorical, time-dependent or series-dependent) can be easily injected into the model
  4. seq2seq seems natural for this task: we predict next values, conditioning on joint probability of previous values, including our past predictions. Use of past predictions stabilizes the model, it learns to be conservative, because error accumulates on each step, and extreme prediction at one step can ruin prediction quality for all subsequent steps.
  5. Deep Learning is all the hype nowadays

Feature engineering

I tried to be minimalistic, because RNN is powerful enough to discover and learn features on its own. Model feature list:

  • pageviews (spelled as 'hits' in the model code, because of my web-analytics background). Raw values transformed by log1p() to get more-or-less normal intra-series values distribution, instead of skewed one.
  • agent, country, site - these features are extracted from page urls and one-hot encoded
  • day of week - to capture weekly seasonality
  • year-to-year autocorrelation, quarter-to-quarter autocorrelation - to capture yearly and quarterly seasonality strength.
  • page popularity - High traffic and low traffic pages have different traffic change patterns, this feature (median of pageviews) helps to capture traffic scale. This scale information is lost in a pageviews feature, because each pageviews series independently normalized to zero mean and unit variance.
  • lagged pageviews - I'll describe this feature later

Feature preprocessing

All features (including one-hot encoded) are normalized to zero mean and unit variance. Each pageviews series normalized independently.

Time-independent features (autocorrelations, country, etc) are "stretched" to timeseries length i.e. repeated for each day by tf.tile() command.

Model trains on random fixed-length samples from original timeseries. For example, if original timeseries length is 600 days, and we use 200-day samples for training, we'll have a choice of 400 days to start the sample.

This sampling works as effective data augmentation mechanism: training code randomly chooses starting point for each timeseries on each step, generating endless stream of almost non-repeating data.

Model core

Model has two main parts: encoder and decoder.


Encoder is cuDNN GRU. cuDNN works much faster (5x-10x) than native Tensorflow RNNCells, at the cost of some inconvenience to use and poor documentation.

Decoder is TF GRUBlockCell, wrapped in tf.while_loop() construct. Code inside the loop gets prediction from previous step and appends it to the input features for current step.

Working with long timeseries

LSTM/GRU is a great solution for relatively short sequences, up to 100-300 items. On longer sequences LSTM/GRU still works, but can gradually forget information from the oldest items. Competition timeseries is up to 700 days long, so I have to find some method to "strengthen" GRU memory.

My first method was to use some kind of attention. Attention can bring useful information from a distant past to the current RNN cell. The simplest yet effective attention method for our problem is a fixed-weight sliding-window attention. There are two most important points in a distant past (taking into account long-term seasonality): 1) year ago, 2) quarter ago.


I can just take encoder outputs from current_day - 365 and current_day - 90 timepoints, pass them through FC layer to reduce dimensionality and append result to input features for decoder. This solution, despite of being simple, considerably lowered prediction error.

Then I averaged important points with their neighbors to reduce noise and compensate uneven intervals (leap years, different month lengths): attn_365 = 0.25 * day_364 + 0.5 * day_365 + 0.25 * day_366

Then I realized that 0.25,0.5,0.25 is a 1D convolutional kernel (length=3) and I can automatically learn bigger kernel to detect important points in a past.

I ended up with a monstrous attention mechanism, it looks into 'fingerprint' of each timeseries (fingerprint produced by small ConvNet), decides which points to attend and produces weights for big convolution kernel. This big kernel, applied to decoder outputs, produces attention features for each prediction day. This monster is still alive and can be found in a model code.

Note, I did'nt used classical attention scheme (Bahdanau or Luong attention), because classical attention should be recalculated from scratch on every prediction step, using all historical datapoints. This will take too much time for our long (~2 years) timeseries. My scheme, one convolution per all datapoints, uses same attention weights for all prediction steps (that's drawback), but much faster to compute.

Unsatisfied by complexity of attention mechanics, I tried to remove attention completely and just take important (year, halfyear, quarter ago) datapoints from the past and use them as an additional features for encoder and decoder. That worked surprisingly well, even slightly surpassing attention in prediction quality. My best public score was achieved using only lagged datapoints, without attention.


Additional important benefit of lagged datapoints: model can use much shorter encoder without fear of losing information from the past, because this information now explicitly contained in features. Even 60-90 days long encoder still gives acceptable results, in contrast to 300-400 days required for previous models. Shorter encoder = faster training and less loss of information

Losses and regularization

SMAPE (target loss for competition) can't be used directly, because of unstable behavior near zero values (loss is a step function if truth value is zero, and not defined, if predicted value is also zero).

I used smoothed differentiable SMAPE variant, which is well-behaved at all real numbers:

epsilon = 0.1
summ = tf.maximum(tf.abs(true) + tf.abs(predicted) + epsilon, 0.5 + epsilon)
smape = tf.abs(predicted - true) / summ * 2.0

losses_0 losses_1

Another possible choice is MAE loss on log1p(data), it's smooth almost everywhere and close enough to SMAPE for training purposes.

Final predictions were rounded to the closest integer, negative predictions clipped at zero.

I tried to use RNN activation regularizations from the paper "Regularizing RNNs by Stabilizing Activations", because internal weights in cuDNN GRU can't be directly regularized (or I did not found a right way to do this). Stability loss didn't work at all, activation loss gave some very slight improvement for low (1e-06..1e-05) loss weights.

Training and validation

I used COCOB optimizer (see paper Training Deep Networks without Learning Rates Through Coin Betting) for training, in combination with gradient clipping. COCOB tries to predict optimal learning rate for every training step, so I don't have to tune learning rate at all. It also converges considerably faster than traditional momentum-based optimizers, especially on first epochs, allowing me to stop unsuccessful experiments early.

There are two ways to split timeseries into training and validation datasets:

  1. Walk-forward split. This is not actually a split: we train on full dataset and validate on full dataset, using different timeframes. Timeframe for validation is shifted forward by one prediction interval relative to timeframe for training.
  2. Side-by-side split. This is traditional split model for mainstream machine learning. Dataset splits into independent parts, one part used strictly for training and another part used strictly for validation.


I tried both ways.

Walk-forward is preferable, because it directly relates to the competition goal: predict future values using historical values. But this split consumes datapoints at the end of timeseries, thus making hard to train model to precisely predict the future.

Let's explain: for example, we have 300 days of historical data and want to predict next 100 days. If we choose walk-forward split, we'll have to use first 100 days for real training, next 100 days for training-mode prediction (run decoder and calculate losses), next 100 days for validation and next 100 days for actual prediction of future values. So we actually can use only 1/3 of available datapoints for training and will have 200 days gap between last training datapoint and first prediction datapoint. That's too much, because prediction quality falls exponentially as we move away from a training data (uncertainty grows). Model trained with a 100 days gap (instead of 200) would have considerable better quality.

Side-by-side split is more economical, as it don't consumes datapoints at the end. That was a good news. Now the bad news: for our data, model performance on validation dataset is strongly correlated to performance on training dataset, and almost uncorrelated to the actual model performance in a future. In other words, side-by-side split is useless for our problem, it just duplicates model loss observed on training data.


I used validation (with walk-forward split) only for model tuning. Final model to predict future values was trained in blind mode, without any validation.

Reducing model variance

Model has inevitably high variance due to very noisy input data. To be fair, I was surprised that RNN learns something at all on such noisy inputs.

Same model trained on different seeds can have different performance, sometimes model even diverges on "unfortunate" seeds. During training, performance also wildly fluctuates from step to step. I can't just rely on pure luck (be on right seed and stop on right training step) to win the competition, so I had to take actions to reduce variance.


  1. I don't know which training step would be best for predicting the future (validation result on current data is very weakly correlated with a result on a future data), so I can't use early stopping. But I know approximate region where model is (possibly) trained well enough, but (possibly) not started to overfit. I decided to set this optimal region bounds to 10500..11500 training steps and save 10 checkpoints from each 100th step in this region.
  2. Similarly, I decided to train 3 models on different seeds and save checkpoints from each model. So I have 30 checkpoints total.
  3. One widely known method for reducing variance and improving model performance is SGD averaging (ASGD). Method is very simple and well supported in Tensorflow - we have to maintain moving averages of network weights during training and use these averaged weights, instead of original ones, during inference.

Combination of all three methods (average predictions from 30 checkpoints using averaged model weights in each checkpoint) worked well, I got roughly the same SMAPE error on leaderboard (for future data) as for validation on historical data.

Theoretically, one can also consider two first methods as a kind of ensemble learning, that's right, but I used them mainly for variance reduction.

Hyperparameter tuning

There are many model parameters (number of layers, layer depths, activation functions, dropout coefficents, etc) that can be (and should be) tuned to achieve optimal model performance. Manual tuning is tedious and time-consuming process, so I decided to automate it and use SMAC3 package for hyperparameter search. Some benefits of SMAC3:

  • Support for conditional parameters (e.g. jointly tune number of layers and dropout for each layer; dropout on second layer will be tuned only if n_layers > 1)
  • Explicit handling of model variance. SMAC trains several instances of each model on different seeds, and compares models only if instances were trained on same seed. One model wins if it's better than another model on all equal seeds.

Contrary to my expectations, hyperparamter search did not found well-defined global minima. All best models had roughly the same performance, but different parameters. Probably RNN model is too expressive for this task, and best model score depends more on the data signal-to-noise ratio than on the model architecture.

Anyway, best parameters sets can be found in file

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