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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55 views

What Past Approaches is the "Taylor Swift" Paper Referring To? [closed]

(My previous question regarded which theorem of probability was equation (2) referring to). This paper mentions that temporal forecasting is meant to solve an integral equation as denoted by (2) (...
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1answer
47 views

Is a Transformer a good choice for multivariate signal classification?

I am working on a problem regarding the multi-classification of multivariate time signals. So I have multiple signals and try to train an algorithm on them. My current approach is to build a neural ...
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35 views

How to normalize the IMU timestep dataset?

I have a timestep dataset of IMU data that I need to normalize. I learned about commonly used methods, but I don't know what I choose. I have a dataset with shape (batch_size, timesteps, features) ... ...
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17 views

What does "These designs employ skip connections to avoid a situation where the shortest path between time steps increases" mean?

Less popular alternatives include adding layers to the connections from input to the hidden state, between hidden states, or from the hidden state to the output. These designs employ skip connections ...
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2answers
40 views

How do sine and cosine transforms help in extracting frequencies in time series forecasting models?

I'm trying to learn how time series forecasting models work and while reading a tutorial off the TensorFlow website I came across these algorithms. I don't quite understand what the article means by &...
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50 views

Is my dataset a time series dataset? and should I use an LSTM?

I have a dataset where I am recording temperature after every 4milliseconds till 500 and another feature "conductivity value". The length of the dataset is around a 1000 rows. I need to find ...
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11 views

preprocessing of time series data, each line consist of a time series

Imagine having a dataset (almost 100000 observations) composed of 365 columns (1 year) and each index (or observation) will then be representing time-series data. In my case, each observation (time-...
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1answer
21 views

Neural network for recognizing ship types based on location series

I am building a neural network for recognizing ship types based on a 1000-long series of location data (latitude-longitude, normalized to account for different km/longitude° metrics, so that vector ...
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155 views

What parameters or hyper-parameters of my model for time-series should I change to improve the MAE?

The following time series exercise is about writing the best possible model, minimizing the MAE. Helper functions normalize_series, ...
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1answer
44 views

In a Temporal Convolutional Network, how is the receptive field different from the input size?

I'm playing around with TCN's lately and I don't understand one thing. How is the receptive field different from the input size? I think that the receptive field is the time window that TCN considers ...
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12 views

How sensitive are LSTM's to random zero values in its target feature when training?

I have worked with lstm's in the past, specifically for time series forecasting. However, the target feature in these time series were relatively "stable". With the loosely defined "...
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7 views

Training and sampling for static model in multivariate time series

Let's suppose I have two time series $x_t$ and $y_t$. I also assume there is an underlying static model of the form: $$ y_t=f(x_t) + \epsilon_t $$ As I said I consider the model a static model meaning ...
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10 views

Categorical Location based Time Series data Prediction using LSTMs

I have some time series data with ActivityType as location shared below. Each CaseID has 6 unique values with different ...
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86 views

Discrepencies between the TimeGan paper and the code?

I recently read the paper Time-Series Generative Neural Networks and found the results that they reported quite promising (https://proceedings.neurips.cc/paper/2019/file/...
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20 views

Dealing with images of multivariate time series

Assuming we have the following input multivariate series: number_of_samples, number_of_timestamps, number_of_features Upon conversion to images using any of the ...
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1answer
34 views

How to construct a model to predict the value of a time series $y_t$ that depends from other time series $\bar{X}_t$?

I would like to know what are the standard approach to construct a model to predict the value of a time series $y_t$ that depends from other time series $\bar{X}_t$. I use to see around that for this ...
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1answer
35 views

Why use sin/cos to give periodicity in time series prediction

In this tutorial https://www.tensorflow.org/tutorials/structured_data/time_series#feature_engineering (scroll down a bit to "Time" heading), they take the sin/cos of the time index, and give ...
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1answer
130 views

Transformer model is very slow and doesn't predict well

I created my first transformer model, after having worked so far with LSTMs. I created it for multivariate time series predictions - I have 10 different meteorological features (temperature, humidity, ...
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19 views

Time series forecasting with some challenges

I'm attempting to devise a strategy to make time series forecasts based on costs accumulated over time. My dataset contains about 7500 time-series sequences (call it an instance for now), each having ...
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35 views

LSTM and CNN - feature engineering and order for time series classification

My questions are related to multivariate time series classification, hence it may differ from forecasting problems. I can have either variable (entire history of the series) or fixed time steps (...
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1answer
33 views

Transforming a complex if-else decision-making to ML

I have a time series classification problem that uses a series of if-else statements to arrive at a particular label. I am attempting to use ML/DL to make the system simpler. So far, I have tried ...
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2answers
27 views

Is seq2seq the best model when input/output sequences have fixed length?

I understand that seq2seq models are perfectly suitable when the input and/or the output have variable lengths. However, if we know exactly the input/output sequence lengths of the neural network. Is ...
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19 views

Train separate AutoEncoder's on each class or one AE for all classes to learn features?

I'm working on a project where the dataset contains time series of three classes, depending on the shape of the series. I want to learn the representations of these series as vectors, so naturally I ...
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1answer
251 views

Is there a way to parallelise the RL training on multiple stocks to avoid the memory issue?

I have some plans in working with Reinforcement Learning in order to predict the stock price movement. For a stock like TSLA some training features might be the pivot price values and the set of the ...
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57 views

Positional Encoding in Transformer on multi-variate time series data hurts performance

I set up a transformer model that embeds positional encodings in the encoder. The data is multi-variate time series-based data. As I just experiment with the positional encoding portion of the code I ...
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34 views

How Long Short Term Memory (LSTM) work for time series classification?

I first got the concept of LSTM on how it works word to word prediction etc. However, I want to know how it work with the time-series classification. For example I have the follwing data (see image ...
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27 views

Time series forecasting for multiple objects with common features

I know the title of this question may raise an eyebrow, but I can't find the technical terms to define or investigate the actual problem. To demonstrate my problem with a simple hypothetical scenario: ...
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1answer
20 views
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14 views

End-to-end learning using LSTM-AE

I want to use prediction models like LSTM-AE to predict time-series data. The feature that the neural network should learn is in frequency between 40-60Hz. So, in order to learn the feature more ...
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1answer
138 views

What is the proper way to process continuous sequence data, such as time-series, using the Transformer?

What is the right way to input continuous, temporal (time-series) data into the Transformer? Assume we're using the basic TransformerBlock here. Since data is continuous with no tokens, Token ...
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1answer
54 views

Why doesn't the LSTM model improve the time-series forecasting significantly with respect to the MLP model?

I have recently started learning time series forecasting. I have a dataset of the weekly payment history of 10k clients over 1 year, and I want to predict the future 5 payments for a test set of 1k ...
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1answer
37 views

What is a better approach to perform predictions of time-series several values ahead?

Suppose one has a time series (univariate or multivariate) and the goal is to predict values of these series several steps ahead. I see two possible strategies: Create a model (recurrent, ...
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22 views

Multiple Entities, Multivariate, Multi-step - Time Series Prediction - Python

My goal is to create a time series model with Multiple Entities - I have multiple products with pre orders and they all have the a similar bell shaped curve peeking at the release date of the product ...
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10 views

Feature selection by Simple regression vs finite impulse response (FIR) method (on TIME Series analysis)

We are working on prediction one company production estimation and the main field of works is like stock market prediction(Time series analysis and process data). So I have some comment on using ...
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13 views

Any good examples/projects of how to do time series forcasting for modern applications?

Time series have been widely analyzed by statistical models (e.g., ARIMA) and machine learning techniques (e.g., LSTM). While the textbooks have provided detailed explanations on the individual models/...
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11 views

What are the typical things in data that I would have to look for, when implementing survival models using machine learning?

Problem Scenario I am working on an industry-specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever ...
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36 views

LSTM Recursive Forecast

I am confused about the way the LSTM networks work when forecasting with a horizon that is not finite, but I'm rather searching for a prediction in whatever time in future. In physical terms, I would ...
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22 views

Good metrics and losses to use for Sequence-to-Sequence model for time-series prediction/forecasting

I am developing a sequence-to-sequence LSTM model for multi-step time series forecasting. I have the basic model working, so now I need to drill down on which loss function and evaluation metrics to ...
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10 views

How to measure(classify) the speed of oncoming traffic via Computer Vision and Neural Networks?

Suppose I have different videos of the same car sometimes moving slow, sometimes moving fast, say, at 50Kmph as slow and 60Kmph as fast. (Assume the background is a green screen and the car doesn't ...
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14 views

What does it mean when predicted results are constant values?

I'm practicing with some data with a LSTM neural nets to come up with predicted data, comparing with actual data. I generated an image to show what I came up with. The blue line is actual data, and ...
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46 views

Forecasting of spatio-temporal event data

I’m currently working on my dissertation which is centred around forecasting social conflict events. I’m using data from GDELT (Global Database of Events, Tone, and Language) to develop my forecasting ...
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23 views

What is a good approach to apply smoothing techniques to time series with big changes and seasonality?

As far as I understand, it is always a good idea to apply a smoothing technique to a raw time series before training a model with it. However, I have a time series with big changes in magnitude and ...
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19 views

Is my dataset unlearnable, or is my LSTM model not smart enough?

I have time-series data obtained from a video. The data is composed of bitrate and corresponding label pairs for each timestamp: The distribution over the first 30 seconds is as follows: I have ...
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1answer
88 views

Rescaling time-series data with very spiky pattern for training data in LSTM network

I am working with some time-series hydrology data. Our goal is to forecast the time series forward, meaning predicting the data 1 month, 3 months ,6 months into the future. The data itself(image below)...
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36 views

Is there a technique for analyzing the relationship between time-series clusters?

I have two time-series datasets (temperature and speed of the vehicle). I will use Agglomerative Hierarchical Clustering and DTW to cluster both datasets. I am looking for a technique (like regression ...
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18 views

Is there a different approach, other than MAML combined with LSTM, for meta-regression of time-series data?

I am working on the calibration of low-cost air sensor data (a time series regression problem). My primary focus is to use some meta/ few-shot learning approach to solve this problem with fewer data. ...
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1answer
50 views

What kind of neural network should I build to classify each instance of a time series sequence?

Let's say I have the time-series dataset below-left. I would like to train a model in such a way that, if I feed the model with an input like the test sequence below, it should be able to classify ...
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1answer
50 views

Recommended Time serie forecasting model for Fibonacci levels classification

I have a set of time series data which gives me fibonacci levels and the duration at which the value is at this level. Data structure to look like: ...
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19 views

Which is the Best Way to Create Training Sequences for LSTM-based Class Prediction on Time-series Data?

Let's say I have time-series data in the following way. I need to create training sequences of a fixed length as an input to my LSTM model on PyTorch. ...
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21 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: ...