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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20 views

What would be a suitable loss function to solve the problem of partitioning an array into sub-arrays?

I have a long segment/array (e.g. 100000 samples) as input. Regardless of the method, I need to output a partition of this segment into $k$ sub-segments, which do not overlap, and whose union can but ...
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16 views

Multiple Time Series (multivariate) Regression

I am trying to do regression using data from multiple time-series. From what I see some tutorials propose to stack time-series data one into another in a single row of features. I looks to me ...
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1answer
29 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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1answer
28 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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16 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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1answer
22 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
30 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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1answer
25 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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17 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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18 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
53 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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20 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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1answer
48 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
80 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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41 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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25 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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30 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
103 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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30 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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22 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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29 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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33 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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106 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
25 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
70 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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22 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
90 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
69 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
25 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 ...
3
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0answers
36 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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35 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 ...
2
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0answers
43 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 ...
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3answers
95 views

How to predict time series with accuracy?

I am trying to predict Forex time series. The nature of the market is that 80% of the time the price can not be predicted, but in 20% of the time it can be. For example, if the price drops down very ...
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22 views

What's the best method to predict/generate signal from one sensor (source) to signal from another another (target)?

I was wondering what is the best method out there to find relationship between two 1D signals so that I can predict/generate one (source) from the other (target). For example, let's say that in ...
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0answers
14 views

How to handle multiscale time series for DNN?

I have a time-series signal with data sampled every minute. I've made different scales of the original signal like 30 minutes, 1 hour and ... . Now because the lengths of these signals are different I'...
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1answer
46 views

time-series prediction : loss going down, then stagnates with very high variance

I am trying to design a model based on LSTM cells to do time-series prediction. The ouput value is an integer in [0,13]. I have noticed that one-hot encoding it and ...
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0answers
22 views

RNN/LSTM with a large amounts of data

I have sequence data that's quite large - 4x65k per sample. I'm interested in doing classification problems. The number of classes is moderate - ~27 or so What is the suggestion for dealing with this ...
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0answers
15 views

Generation of realistic real-valued sequences using Wasserstein GAN fails

My goal is to generate artificial sequences of real-valued data (e.g. time series) with GANs. Starting simple I tried to generate realistic sine-waves using a Wasserstein GAN. But even on this simple ...
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1answer
35 views

Convert input dataset given in hex addresses to int

I have created an LSTM Neural Network which take as input the following format in an .csv file ...
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1answer
58 views

What's the best architecture for time series prediction with a long dataset?

I have to build a neural network without any architecture limitations which have to predict the next value of a time series. The dataset is composed of 400.000 values, which are given in hex format. ...
2
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1answer
35 views

Predicting a day's data

I have a dataset containing timestamp and temperature. For each day, I have 1440 values viz., I have data for every minute of that day(60minutes * 24hrs = 1440). The Dataset looks like this: As an ...
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0answers
37 views

How to exploit translational symmetry for extrapolation in video generation using machine learning

I'll try to rephrase my problem in the context of video processing. Imagine that initial frame of video has some translational symmetry. The frame evolves according to an update rule. I generate a ...
3
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2answers
207 views

Image vs Non-Image Data in CNN

When using CNN on non-image(times series) data prediction, what are some constraints or things to look out for as compared to image data? To be more precise, I ...
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0answers
18 views

Deep Learning on time series tabular data

In this new book release, at the top of page 51 the authors mention that to do deep learning on time series tabular data the developer should structure the tensors such that the channels represent the ...
3
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1answer
56 views

Can non-sequential deep learning models outperform sequential models in time series forecasting?

Can a CNN (or other non-sequential deep learning models) outperform LSTM (or other sequential models) in time series data? I know this question is not very specific, but I experienced this when ...
2
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1answer
47 views

Feature extraction timeseries, model compatibility

I've got a timeseries with sensor data (e.g. accelerometer and gyroscope). I now want to extract the activity out of it (e.g. walking, standing, driving, ...). I Followed this Jupyter Notebook. But ...
5
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1answer
439 views

What is the “semantic level”?

I am reading the paper Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction (2018) by Yunzhe Tao et al. In this paper, they use several times the expression "semantic ...
2
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1answer
871 views

ValueError: Error when checking target: expected dense_3 to have shape (1,) but got array with shape (2,)

I am trying to build a CNN model on Keras. The data has a dimension of 921 rows × 10000 columns. Here is the code: ...
2
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0answers
25 views

Choosing neural network output for prediction (regression) of a dynamical system

I’m trying to train a neural network to approximate the output of a dynamical system $dy/dt=f\left(y(t), u(t) \right)$, namely, given $y(0)$ and $u(t_i), i=1,2...N$ I want the network to predict $y(...