Last Updated on August 5, Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption.

Unlike other machine learning algorithms, long short-term memory recurrent neural networks are capable of automatically learning features from sequence data, support multiple-variate data, and can output a variable length sequences that can be used for multi-step forecasting. In this tutorial, you will discover how to develop long short-term memory recurrent neural networks for multi-step time series forecasting of household power consumption.

Discover how to build models for multivariate and multi-step time series forecasting with LSTMs and more in my new bookwith 25 step-by-step tutorials and full source code. Note : This is a reasonably advanced tutorial, if you are new to time series forecasting in Python, start here. If you are new to using deep learning for time series, start here. If you really want to get started with LSTMs for time series, start here. Learn how in this tutorial:. The data was collected between December and November and observations of power consumption within the household were collected every minute.

Active and reactive energy refer to the technical details of alternative current. A fourth sub-metering variable can be created by subtracting the sum of three defined sub-metering variables from the total active energy as follows:.

Download the dataset and unzip it into your current working directory. This will allow us to work with the data as one array of floating point values rather than mixed types less efficient. A very simple approach would be to copy the observation from the same time the day before.

Now we can create a new column that contains the remainder of the sub-metering, using the calculation from the previous section. We can now save the cleaned-up version of the dataset to a new file; in this case we will just change the file extension to. Tying all of this together, the complete example of loading, cleaning-up, and saving the dataset is listed below. In this section, we will consider how we can develop and evaluate predictive models for the household power dataset.

## Time Series Prediction Using LSTM Deep Neural Networks

This requires that a predictive model forecast the total active power for each day over the next seven days. Technically, this framing of the problem is referred to as a multi-step time series forecasting problemgiven the multiple forecast steps. A model that makes use of multiple input variables may be referred to as a multivariate multi-step time series forecasting model. A model of this type could be helpful within the household in planning expenditures.

It could also be helpful on the supply side for planning electricity demand for a specific household. This framing of the dataset also suggests that it would be useful to downsample the per-minute observations of power consumption to daily totals.

This is not required, but makes sense, given that we are interested in total power per day. We can achieve this easily using the resample function on the pandas DataFrame.

We can then calculate the sum of all observations for each day and create a new dataset of daily power consumption data for each of the eight variables. We can use this as the dataset for fitting and evaluating predictive models for the chosen framing of the problem. It is common with multi-step forecasting problems to evaluate each forecasted time step separately. This is helpful for a few reasons:.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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This repo contains preliminary code in Python 3 for my blog post on implementing time series multi-step ahead forecasts using recurrent neural networks in TensorFlow.

A version of this blog can also be found below, see in particular the high-level code comments. Specifically, I'd like to perform multistep ahead forecasts and I was wondering how to do this 1 with RNNs in general and 2 in TF in particular.

Here I summarize my insights. In particular I give a short overview over some available approaches. Furthermore, I provide code on GitHub which evaluates two simple approaches on real data. To give a specific example of the problem: for our Max Planck Tuebingen campus cafeteria I'd like to forecast congestion more specifically: queue length. The cafeteria opens at Say it'sand I'd like to predict congestion for the remaining open time till In my current data, congestion is measured every 5 minutes.

Before going into details, let me emphasize that these are just preliminary notes. I don't have a full overview over problem and available approaches yet.

Feel free to get in touch if you have any comments! From my understanding, this is not in general possible in classical neural net-based approaches similar, I guess, as it would be with other "discriminative" methods. In a sense: whatever mapping you want, you have to train it explicitly.

To predict multiple steps ahead using RNNs, various approaches have been proposed nonetheless. Let us follow [2][3] to briefly introduce them note that [2][3] do not explicitly restrict to RNNs - could be non-recursive NNs as well :. Generally, for me it would be interesting if there are other, more principled, approaches, so please get in touch if you know of any. I implemented a first version of the recursive versus the joint approach in TF and applied it to data from our campus cafeteria which I mentioned above.

Note that for now, I focused on intra-day forecasts, while the more generic use case for RNNs and the recursive approach would probably be between-day forecasts. The recursive approach was a bit more tricky and I haven't found a fully convinccing solution. I coded one model function which has two "modes", distinguishable via parameters: 1 a classical 1-step ahead RNN LSTM, to be specific and 2 arbitrary steps ahead predictions based on the recursive approach.

Then I train the model via 1store the weights as a checkpoint, and define a new estimator based on the mode 2but with the weights loaded from the training of 1. The code is available on GitHub.This is a tutorial on how to apply a Time Series Model on energy dataset. It consists of years of hourly electricity load data from the New England ISO and also includes hourly temperature data. We just choose part of electricity load data whose date range is from to to complete our tutorial.

And, we load the dataset into MySQL manually. The energy data set contains two features, one is the date-time column, and the other is electricity load data. Before that, we need to scale the data first into the range of 0, 1. We apply the min-max normalization in this project. By the way, it should be noted that data scaling must be done before the data reconstruct shown in the next step.

After that, we need to convert the data type of reconstructed data above into float in case some error reported in the later stage. In this stage, we separate our dataset into train, validation and test sets, the proportions are We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data.

After the model has finished training, we predict the test set by the trained model. Due to the output of this task is multi-outputs, we concatenate the target cols into a column.

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The standard SQL statements for specifying the training data like:. We can see that the target column of the predicted table is a column that consists of multi-column data. In order to get the data of every single column, we need to split the target column by following SQL statement. Finally, since our data is a dimensional range between 0 and 1.

In order to obtain the original dimension data, we need to denormalize the predicted results, as following SQL statement shows. SQLFlow Menu. Predict one or more time-step ahead electricity load data, using historical load data only. PART 1 Prepare Data The energy data set contains two features, one is the date-time column, and the other is electricity load data.

We can have a quick peek of the raw data by running the following standard SQL statements.Hi, Could you please tell me how to predict the next 10 days in future?

Hi, I am also looking on how to predict the future 10 days. Any help on the same will be really helpful. Thanks in advance. I am new to deep learning and LSTM. I have a very simple question. I have taken a sample of demands for 50 time steps and I am trying to forecast the demand value for the next 10 time steps up to 60 time steps using the same 50 samples to train the model.

**TensorFlow 2.0 Tutorial for Beginners 19 - Multi Step Prediction using LSTM - Time Series Prediction**

Can anybody help me with this issue? Dear lukovkin, Suppose I have multiple time series as input and I need to predict all these time series at once for the next 10 days, how should I reshape the input and target datasets? Skip to content. Instantly share code, notes, and snippets. Code Revisions 2 Stars 26 Forks Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. Time series prediction with multiple sequences input - LSTM - 1.

Time Series Testing import keras. This comment has been minimized. Sign in to view. Copy link Quote reply. Can you help me solve this issue? I have attached my code below. Thank you in advance. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment.

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You signed out in another tab or window. Time Series Testing. Call back to capture losses. Callback :. You should get data frames with prices somewhere, e.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.

It only takes a minute to sign up. I would like to forecast the heat load of a district heating network given its past values, the temperature and the 3-day ahead forecast of the temperature with an LSTM RNN.

The data is hourly and I try to forecast the sequence of the next 72 values, so 72h into the future. I was wondering which approach to chose here? Using the historical load, temperature and temperature forecast as input and forecasting the sequence of the 72h heat load into the future at once via an output layer with 72 neurons.

Using the historical load and temperature forecast as input and forecasting one step into the future. Then iteratively forecasting the next value with the forecasted heat load and the temperature forecast.

That's the 2 options I have thought about so far. Are they somehow implementable or do I miss something important? Unfortunately, the literature I have found did not really suggest what method to chose on predictions for more than one-time step into the future and some kind of exogenous variables that are known in the future. I prefer option 1 where you will predict the next 72 hours at once with all the history and future data like the forecast. I do not see why option 2 would outperform option 1 in theory.

And yes, it is doable. Take a look at this paper. This is part of the trial and error process, so you will know which method is better once you try them.

### LSTM for international airline passengers problem with window regression framing

Both approaches are implementable and have their pros and cons as shown in this excellent thesis on this subject. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 9 months ago.

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If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. DTS is a Keras library that provides multiple deep architectures aimed at multi-step time-series forecasting.

The Sacred library is used to keep track of different experiments and allow their reproducibility. The setup. Thus, before installing DTSyou have to manually install:. This choice has been taken in order to avoid any possible problem for the user. The package includes several deep learning architectures that can be used for multi step-time series forecasting.

The package provides also several utilities to cast the forecasting problem into a supervised machine learning problem. Specifically a sliding window approach is used: each model is given a time window of size n T and asked to output a prediction for the following n O timesteps see Figure below. If True, multiple experiments are run, each with a different combination of hyperparamters.

The process terminates when all possible combinations of hyperparamers have been explored. The main function for your model should always look similar to this one:. Mongo Observer which stores all information in a MongoDB If you want to use the file-based logger then launch the script with the additional argument --observer file once again, the default choice is --observer mongodb.

If you want to train a model using pretrained weights just run the model providing the paramter --load followed by the fullpath to the file containing the weights. With DTS you can model your input values in many diffrent ways and then feed them to your favourite deep learning architectures. See how to format your data or check out the examples in dts.

The training and inference procedures are the same. Recursive : The RNN is trained to predict the next step, i.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here.

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## How to Develop Multi-Step LSTM Time Series Forecasting Models for Power Usage

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Specifically, I have two variables var1 and var2 for each time step originally. Having followed the online tutorial hereI decided to use data at time t-2 and t-1 to predict the value of var2 at time step t.

As sample data table shows, I am using the first 4 columns as input, Y as output. The code I have developed can be seen herebut I have got three questions. Update: LSTM result blue line is the training seq, orange line is the ground truth, green is the prediction.

From your table, I see you have a sliding window over a single sequence, making many smaller sequences with 2 steps. Assuming you're using that table as input, where it's clearly a sliding window case taking two time steps as input, your timeSteps is 2. You should probably work as if var1 and var2 were features in the same sequence:.

We do not need to make tables like that or build a sliding window case. That is one possible approach. If on one hand your model is capable of learning long time dependencies, allowing you not to use windows, on the other hand, it may learn to identify different behaviors at the beginning and at the middle of a sequence.

In this case, if you want to predict using sequences that start from the middle not including the beginningyour model may work as if it were the beginning and predict a different behavior. Using windows eliminate this very long influence. Which is better may depend on testing, I guess. Here, we will need to separate two models, one for training, another for predicting.

This means that for each input step, we will get an output step. Have your input data shaped as 1,21 sequence, taking the steps from 1 to Both vars in the same sequence 2 features. Have your target data Y shaped also as 1,2taking the same steps shifted, from 2 to You can make an input with lengthfor instance shape: 1,2 and predict just the next step:.

Actually you can't just feed in the raw time series data, as the network won't fit to it naturally. The current state of RNNs still requires you to input multiple 'features' manually or automatically derived for it to properly learn something useful.

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