Skip to main content
19 votes
Accepted

Can Reinforcement learning be applied for time series forecasting?

Yes, but in general it is not a good tool for the task, unless there is significant feedback between predictions and ongoing behaviour of the system. To construct a reinforcement learning (RL) ...
Neil Slater's user avatar
8 votes

Is time series forecasting possible with a transformer?

Transformers can be used for time series forecasting. See the following articles: Adversarial Sparse Transformer for Time Series Forecasting, by Sifan Wu et al. Deep Transformer Models for Time ...
Brian O'Donnell's user avatar
7 votes
Accepted

How fbprophet cross validation works

Think about how you would verify the model on your own. You could train it on data in the past, stopping before the present, and then ask the model to predict for a period that you already had data ...
Josh Friedlander's user avatar
6 votes

time series forecasting - sliding window method

Try this: Make the data stationary (remove trends and seasonality). Implement PACF analysis on the label data (For eg: Load) and find out the optimal lag value. Usually, you need to know how to ...
Aniruddh Goteti's user avatar
4 votes
Accepted

Aggregation of Discount

Aggregate discount sequences are used to define the stacking logic for aggregate discounts. This logic can define: The order in which aggregate discounts are applied. Which aggregate discounts are ...
dileep balineni's user avatar
4 votes

Are Machine Learning Weather Prediction models better than classic weather forecast?

I'm not a meteorologist but in my humble opinion, Numerical Weather Prediction and especially WRF (stands for Weather Research and Forecasting - a high resolution & mesoscale model, unlike the old ...
UgurZCifci's user avatar
3 votes
Accepted

LSTM future steps prediction with shifted y_train relatively to X_train

Unfortunately it is more likely that this approach itself is bad. It's not the fault of your LSTM or neural netowrk. You may be able to find a lot of online tutorials using RNN/LSTM to predict stock ...
user12075's user avatar
  • 2,284
3 votes

When forecasting time series, how does one incorporate the test data back into the model after training?

I agree with what Emre has commented on your question. If you have enough data, I would try cross-validating your model by training it on different time sections of your data. For example, train your ...
gingermander's user avatar
3 votes

Is there an R tutorial of using LSTM for multivariate time series forecasting?

I like the Jena data example. In multivariate settings, you only need to generate lookbacks over all X. https://blogs.rstudio.com/tensorflow/posts/2017-12-20-time-series-forecasting-with-recurrent-...
Peter's user avatar
  • 7,526
3 votes

Time Series Forecasting Seasonal type

Each time series can be decomposed in at least three elements: Trend Seasonal component Noise An additive model can be explained as: y = Trend + Seasonal + Noise ...
Leevo's user avatar
  • 6,265
3 votes

Time series forecasting dilemma. Could feature engineering overcome time dependency?

They all start from the same assumption: time series forecasting can't be treated as a regression/classification problem. It is time dependent, which means our target y at time t depends on what the ...
Leevo's user avatar
  • 6,265
3 votes
Accepted

How can I explain this chart showing 5-days moving average?

Yes it makes sense, a moving average makes the curve "smoother" in the sense that it's less sensitive to short variations. This usually makes it easier to observe the general tendency. You ...
Erwan's user avatar
  • 25.5k
3 votes

How can I explain this chart showing 5-days moving average?

Moving averages will give you a smoother time series so that a trend is easier to see by eye. This approach makes sense when you’re exploring the data. The next step is to try to comment on where the ...
Nicholas James Bailey's user avatar
3 votes

Difference between sequence length and batch size in time series forecasting

Let's take a TS data = [ 1, 2, 3, 4, 5, 7, 8, 9, 10 ] Call the function with these parameters ...
10xAI's user avatar
  • 5,634
3 votes

Is time series forecasting possible with a transformer?

I'm unclear whether transformers are the best tool for time series forecasting. Transformers at the end of the day are just the latest in a series of sequence-to-sequence models with an encoder and ...
Nitin's user avatar
  • 314
3 votes

Is time series forecasting possible with a transformer?

Transformers were originally architected for NLP. However many studies have shown that they CAN be used for time series as well and with great success. Let us look at the differences and similarities ...
Allohvk's user avatar
  • 888
3 votes
Accepted

What reccent alternatives to LSTM are there for regression problems?

Transformers (aka "attention models") are being used in place of LSTMs in many areas, as they generally give better results, and/or are quicker to train. They can be used for regression ...
Darren Cook's user avatar
  • 1,074
3 votes
Accepted

Removing seasonality in time series forecasting

Removing seasonality is not something you are obliged to do. It really depends on the model. The idea of decomposing time series (you are not actually removing seasonality, it is simply a component ...
Oscar's user avatar
  • 458
3 votes
Accepted

Why so discrepancy between ARIMA and LSTM in time series forecasting?

Arima and LSTM are very different and there could be some tips to improve results. Have you tried relative values instead of raw values? For instance: ...
Nicolas Martin's user avatar
3 votes
Accepted

Forecasting sequence data with intermittent peaks

Transformed series, delay until next peak ($\tau_i$) and next peak height ($h_i$) also look better to me. If the correlation between subsequent points is low you may struggle in predicting. Are there ...
Cryo's user avatar
  • 553
3 votes
Accepted

When is it necessary to remove seasonality from multivariate time series?

No, do not remove the seasonality anywhere. The only reason you would want to remove the seasonality before training the model is if you expect the seasonal effect to end in real life. Theoretically, ...
Nemo_the_scientist's user avatar
2 votes

How to predict next year's gross revenue given this year's data?

Predicting next year's revenue from previous years' revenues is Time Series Forecasting. You will need data from many previous years to do this. One year is clearly not enough. Just think about it ...
TQA's user avatar
  • 536
2 votes

VAR model ValueError: x already contains a constant

Your two columns X0 and X4 are constants, i.e. they contain a single value throughout. The model will be trying to find a ...
n1k31t4's user avatar
  • 14.9k
2 votes

Multivariate time series forecasting with LSTM

1)First and most important, do not give Yi(t) history as feature. You will just end in a model that replicates the previous input to minimize the error, a cheating model. For more detail, you can have ...
Ugur MULUK's user avatar
2 votes

How to calculate customer purchase interval and predict next purchase in python?

With Lifetimes it's very straight forward to implement.
Jonathan's user avatar
2 votes

Is an Arma model equivalent to a 1-layer Recurrent Neural Network without activation function?

It's correct. The reason it sounds so weird is that a 1-layer-NN without activation function is simply a linear map, so it's equivalent to any linear model, the only difference being the inputs having ...
Lennart Scharmann's user avatar
2 votes
Accepted

Time-series forecasting

Something simlar to the blue fitted line can be obtained using Holt-Winters model. Check HoltWinters() function from stats ...
Leevo's user avatar
  • 6,265
2 votes

How to forecast time series analysis for more then one dependent variables?

I think you do not need to "predict" counter_id and country_code as variables, what you need is to produce results to every counter and country (and sometimes these results will be zero). The ...
Juan Esteban de la Calle's user avatar
2 votes

Time Series Forecasting for Multiple Customers using one RNN

Here are a few rapidfire ideas: Does the customers affect each other in any way? If that is the case, you need to feed the information of multiple customers at the same time to your RNN. If not, then ...
shamwow's user avatar
  • 121
2 votes
Accepted

How to predict NaN (missing values) of a dataframe using ARIMA in Python?

You may apply Wolfram Language to your project. There is a free Wolfram Engine for developers and with the Wolfram Client Library for Python you can use these functions in Python. I will first create ...
Edmund's user avatar
  • 705

Only top scored, non community-wiki answers of a minimum length are eligible