Questions tagged [time-series]

For questions related to time series analysis or forecasting in the context of AI and, in particular, ML.

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15 views

How to Classify Game Stages Based on Bitrate Time Series Data Using RNN - LSTM

I need suggestions for my project and would be glad if you would give me a hand. I have a dataset of frames obtained from the old-school game DOOM. Each frame in the dataset has the following columns: ...
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24 views

Can I create a Q-table in iterated procedure?

Let's say I have a time series vector (hourly price data), and want to obtain an optimal trading policy. To do so, I need a Q-table. My question is about creating such table from a raw time-series. ...
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21 views

How to train an LSTM to classify based on rare historic event?

I want an LSTM to output one of two classes (Y, N), per frame, based on all the input so far. My original inputs are very long (~100000 samples long, far more than a standard LSTM training can handle ...
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26 views

Predict time series from initial non-time dependant parameters

I'm trying to create an algorithm (neural network) that is able to predict a time series from a set of different parameters that are not given through time. Let's say I have a plane flying under the ...
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27 views

Predicting the probability of a periodically happening event occurring at a given time

I have encountered this problem on how to predict the probability of a periodically happening event occurring at a given time. For example, we have an event called being_an_undergrad. There are many ...
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8 views

Is it possible to get this loss with spikes, when training an LSTM with the cross-entropy on a multi-class classification problem of a time series?

The main question here will be "should I look for a bug?" My setup is a time series multiclass classification task, labeled per frame. I am using an LSTM, feeding inputs and using ...
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21 views

Can RNNs be used to classify these time series into two classes?

My task is to classify into two classes the time series like these shown in the figure. The figure shows one class on the left sub-figure and second one on the right. The series are shown in pairs ...
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1answer
43 views

How to forecast multiple target attributes in Python?

I need to forecast two non-correlated time-series (non-stationary). A sample is presented below: ...
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31 views

How to define a “don't care” class in time series classification in Pytorch?

This is a theoretical question. Setup I have a time series classification task in which I should output a classification of 3 classes for every time stamp t. All ...
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55 views

What model to use to get a robust model to predict next 3 days of sales even for products that have just sold once ever?

PROJECT: I am working on an e-commerce site where digital products can run out so there is need to reorder them 72h before they run out (reordering them sooner is not a problem but having notification ...
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31 views

Multivariate time-series classification with many variables

I am attempting to use time-series classification algorithms for fraud detection applications. I have came across several works in the literature that propose novel techniques for multivariate time-...
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18 views

Multi-objective training vs Transfer learning - pros and cons

I'm solving a sequential prediction task that has multiple features attached to each timestamp, and my goal is to predict one of them. However, the feature's label is highly imbalanced: only about 4% ...
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28 views

Time series analysis using computer vision principles

I'm just starting to explore topics within computer vision and curious if there are any concepts in that area that could be applied to segmenting multivariate time series with the goal of grouping ...
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99 views

How are temporal links made between following sequences in RNN?

Say I use an RNN, whatever is the cell's type, to perform time series classification. It can thus be seen as sequence classification. The time series is split into random, equal size, overlapping ...
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72 views

Model-based RL for time series data

I have time-series data. When I take an action, it impacts the next state, because my action directly determines the next state, but it is not known what the impact is. To be concrete: I have $X(t)$ ...
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53 views

How to do early classification of time series event with small dataset?

I would like to build a real-time binary classifier that can predict an event of interest that is occurring as soon as it starts. These are electromyographic signals, and the event classification ...
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1answer
70 views

How to deal with the time delay in reinforcement learning?

I have a question regarding the time delay in reinforcement learning (RL). In the RL, one has state, reward and action. It is usually assumed that (as far as I understand it) when the action is ...
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72 views

How to handle long sequences with transformers?

I have a time series sequence with 10 million steps. In step $t$, I have a 400 dimensional feature vector $X_t$ and a scalar value $y_t$ which I want to predict during inference time and I know during ...
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51 views

Time series prediction using LSTM and CNN-LSTM: which is better?

I am working on LSTM and CNN to solve the time series prediction problem. I have seen some tutorial examples of time series prediction using CNN-LSTM. But I don't know if it is better than what I ...
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1answer
48 views

Do we need non-linear activation function in neural networks whose task isn't classification?

While researching why we need non linear activation functions, all the explanations revolve around neural network being able to separate values that aren't linearly separable. So I wonder, if we have ...
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36 views

What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)?

I've started to work on time series. I was wondering what would be the best data normalizing and pre-processing technique for non-linear models, specifically, neural networks. One I can think of is ...
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19 views

What's the difference between RNNs and Feed Forward Neural Networks if a fixed size vector can preserve sequential information?

I was watching a Youtube video in which the problem of trying to predict the last word in a sentence was posed. The sentence was "I took my cat for a" and the last word was "walk"....
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25 views

Which type of feature extractor do you suggest to classify sensor data?

I have IMU (Inertial Measurment Unit- 6 axis) sensor data. The sensor attached on a car and 7 different drivers wipe on same path. I want to extract features and classify drivers. Which type of ...
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1answer
88 views

Wind speed forecasting using ARIMA model in Python3

Recently, I started working on time-series models and would mention that I am very new to python and ML as a whole. I tried to implement a time-series model on wind speed data. Being a newbie, I ...
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42 views

Unix timestamps for Recurrent Neural Networks

I want to use RNN for classifying whole sequences of events, generated by website visitors. Each event has some categorical properties and a Unix timestamp: ...
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21 views

Do we need to conduct statistical time series test if we are using Regression Models(Linear Regression) for Forecasting Demand?

While working on open source Time Series Data for Grocery sales where my aim is to perform Demand forecast of Grocery Products, I completed the cleaning of data of Grocery sales data, then in order to ...
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24 views

Time Series Forecasting - Recurrent Neural Networks (tensorflow)

I am attempting to forecast a time series using tensorflow with the following code: ...
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1answer
58 views

How to deal with very, very small time-series?

I have an ensemble of 231 time series, the largest among them being 14 lines long. The task at hand is to try to predict these time-series. But I'm finding this difficult due to the very small size of ...
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21 views

What are some solutions for dealing with time series data that are recorded at uneven intervals?

Let's say I have a time series data which is a bunch of observations that occur at different time stamps and intervals. For example, my observations come from a camera located at a traffic ...
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50 views

Recommendations or resources for neural network/deep learning for time series application?

I know there are quite a few good deep learning books out there, but most explain neural networks and deep learning via application on images. If there are examples/code, they are often done on the ...
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1answer
126 views

Why does not the deepAR model of Amazon require the time series being stationary, as opposed to ARMA model?

As what the title said. Does not deepAR require the time series being stationary?
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44 views

How can I build a deep reinforcement learning model that can be trained with multiple time series datasets

I built a DRL model to trade stocks in the financial market but the number of observations is relatively small and I would like to increase it by training the same model with stocks from several ...
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37 views

How to feed the LSTM with different length for the latest time step?

I am having a training data set for a time-series dataset like below: ...
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31 views

Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
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1answer
205 views

How do I test an LSTM-based reinforcement learning model using any Atari games in OpenAI gym?

I am writing a couple of different reinforcement learning models based on Rainbow DQN or some PG models. All of them internally use an LSTM network because my project is using time series data. I ...
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17 views

Separated LSTMs or a global one for cluster of related features

I have an $n$-dimensional time-series to apply LSTM to, $n$ is the number of features for each time point. These features can be clustered according to their concept, for example $n_1, ..., n_4$ are ...
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38 views

How to train an Encoder-Decoder LSTM for sequence to sequence prediction?

I have a dataset where for each country there is a name (string) and a multivariate time series (all integers). I am trying to use an Encoder-Decoder LSTM to forecast the next time steps in the time ...
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24 views

Input with variable length Classification problem

I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem). The data set looks ...
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30 views

How to calibrate model's prediction given past images?

I want to predict how open is the mouth given a face image. It's a regression problem (0= mouth not open, 1=mouth completely open). And something between 0 and 1 is also allowed. ConvNet works fine ...
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35 views

What are some good loss functions used to minimize extreme errors in regression and time series forecasting?

I'm working on a time series forecasting task, and, in some specific cases, I don't need perfect accuracy, but the network cannot by any means miss by a lot. So, in detriment of a smaller mean error, ...
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258 views

Conditional Variational Autoencoder - NON Image Data

First I would like to expand an issue I've been dealing with way too long: Creating a conditional Variational Autoencoder with continuous variables in non-image data ( more specifically, time series). ...
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1answer
30 views

How to exclude sections of bad data from time-series data before training an LSTM network

I am using LSTM network for predicting IOT time-series data receiving from un-reliable devices and networks. This results in several multiple sections [continuous streak of bad data for several days ...
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1answer
77 views

Why do we use a delay when feeding our input data to the echo state network?

I'm new to working with neural networks and have recently began implementing neural networks for time series forecasting in some of my work. I've been particularly using Echo State Networks and have ...
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29 views

Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?

Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...
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1answer
96 views

What are modern state-of-the-art solutions in prediction of time-series?

I wanted to ask you about the newest achievements in time series analysis (mostly prediction). What state-of-the-art solutions (as in frameworks, papers, related projects) do you know that can be used ...
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1answer
73 views

What method to identify markers in data series via machine learning

I have data that is collected from several different instruments simultaneously that is generally analyzed on a location-by-location basis. A skilled interpreter can identify "markers" in the data ...
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1answer
26 views

Inner working of Bidirectional RNNs

I'm trying to understand how Bidirectional RNNs work. Specifically, I want to know whether a single cell is used with different states, or two different cells are used, each having independent ...
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37 views

How to predict an event (or action) based on a window of time-series measurements?

I have an input vector $X$, which contains a series of measurements within a period, e.g. 100 measurements in 1 sec. The goal is to predict an event, let's say, moving forward, backward or static. I ...
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44 views

Model for supervised sequence classification task

The Problem I am currently working on a sequence classification problem I try to solve with machine learning. The target variable is the current state of a system. This target variable is following a ...
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45 views

Should the RL agent be trained in an environment with real-world data or with a synthetic model?

I want to train a reinforcement learning agent in an environment with parameters (for example, the wind speed, sun irradiation, etc.) that change over time. I have recorded a limited amount of data ...