Bmw 6 Series Features

[Jenn Smotherman]

havebeen reading up a bit on LSTM's and their use for time series and its been interesting but difficult at the same time. One thing I have had difficulties with understanding is the approach to adding additional features to what is already a list of time series features. Assuming you have your dataset up like this:

Now lets say you know you have a feature that does affect the output but its not necessarily a time series feature, lets say its the weather outside. Is this something you can just add and the LSTM will be able to distinguish what is the time series aspect and what isnt?

So absolutely, you can have multiple features at each timestep. In my mind, weather is a time series feature: where I live, it happens to be a function of time. So it would be quite reasonable to encode weather information as one of your features in each timestep (with an appropriate encoding, like cloudy=0, sunny=1, etc.).

If you have non-time-series data, then it doesn't really make sense to pass it through the LSTM, though. Maybe the LSTM will work anyway, but even if it does, it will probably come at the cost of higher loss / lower accuracy per training time.

For example, let's say that in your particular application, you only keep the last output of the LSTM output sequence. Let's say that it is a vector of length 10. You auxiliary input might be your encoded weather (so a scalar). Your merge layer could simply append the auxiliary weather information onto the end of the LSTM output vector to produce a single vector of length 11. But you don't need to just keep the last LSTM output timestep: if the LSTM outputted 100 timesteps, each with a 10-vector of features, you could still tack on your auxiliary weather information, resulting in 100 timesteps, each consisting of a vector of 11 datapoints.

In other cases, as @horaceT points out, you may want to condition the LSTM on non-temporal data. For example, predict the weather tomorrow, given location. In this case, here are three suggestions, each with positive/negatives:

Have the first timestep contain your conditioning data, since it will effectively "set" the internal/hidden state of your RNN. Frankly, I would not do this, for a bunch of reasons: your conditioning data needs to be the same shape as the rest of your features, makes it harder to create stateful RNNs (in terms of being really careful to track how you feed data into the network), the network may "forget" the conditioning data with enough time (e.g., long training sequences, or long prediction sequences), etc.

Include the data as part of the temporal data itself. So each feature vector at a particular timestep includes "mostly" time-series data, but then has the conditioning data appended to the end of each feature vector. Will the network learn to recognize this? Probably, but even then, you are creating a harder learning task by polluting the sequence data with non-sequential information. So I would also discourage this.

Probably the best approach would be to directly affect the hidden state of the RNN at time zero. This is the approach taken by Karpathy and Fei-Fei and by Vinyals et al. This is how it works:

This approach is the most "theoretically" correct, since it properly conditions the RNN on your non-temporal inputs, naturally solves the shape problem, and also avoids polluting your inputs timesteps with additional, non-temporal information. The downside is that this approach often requires graph-level control of your architecture, so if you are using a higher-level abstraction like Keras, you will find it hard to implement unless you add your own layer type.

The question if the model will perform better had I done it Adam's way, is still to be tested. But for people who don't want to go the extra mile, appending non-sequential features to sequential ones works just fine.

I have a created all the required parts including a layout to support building a map series. Currently when I run Map Series > Spatial I get a series and while each page is the correct extent based on the indexed feature, I see the feature I want but also all the other features in that layer within the extent. I'd like to only see the single feature based on the attribute used in the Layout Properties form (ie one feature ID per series page.

Yep That did it. One last thought that will likely lead to a new thread. I have text in my layout as well. At the moment that gets updated as did the map BEFORE applying a page query. So one text field on the layout has all the values of the rows in the it references. Is there a Page Query-like equivalent for the standalone tables that populate text fields?

I'm having trouble getting time series forecasts with DeepAR and Auto ARIMA models when using an external feature. I have provided future values of the external feature, however the model only gives backtests instead of future predictions.

In my case I have a F1 fan site where I can see the amount of people that browse on it. As an external feature I want to give as input the days that there will be a raceday and historical data that shows on which days there has been a raceday in the past.

During training, models train on all the dataset provided and only make backtest forecasts (and not future forecasts), so you shouldn't provide future values of external features in the training dataset.

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I'm working on a problem that outputs the cumulative water production of several water wells. The features I have are both time series (water rate and pump speed as functions of time) and static (depth of the wells, latitude and longitude of the well, thickness of the water bearing zone, etc.)

LSTM_att proposed by Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Modelenter link description here seems to be a good option.

It applies static features to calculate the attention to aggregate the hidden states of time series and also provides a shortcut connection between each hidden state and final state (similar to ResNet). It outperforms baseline LSTM models.

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We can compute many different features on many different time series, and use them to explore the properties of the series. In this chapter we will look at some features that have been found useful in time series exploration, and how they can be used to uncover interesting information about your data. We will use Australian quarterly tourism as a running example (previously discussed in Section 2.5).

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