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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Why can't I reproduce my results in keras using random seed? [closed]

I was doing a task using RNN to predict a time series movement. I want to make my results reproducible. So I strictly followed this post: https://stackoverflow.com/questions/32419510/how-to-get-...
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What model can solve vector to vector prediction?

I am totally newbie into serial prediction. I am think about which model or AI paradigm can be used to do vector to vector prediction? For instance, [1,0,1] ^ [0,1,0] = [1,1,1] Another example could ...
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Finding "look_back" & "look_ahead" hyper-parameters for Seq2Seq models

For Seq2Seq deep learning architectures, viz., LSTM/GRU and multivariate, multistep time series forecasting, it is important to convert the data to a 3D dimension: (batch_size, look_back, ...
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RL-based trading bot: how to deal with overfitting

I've been playing around building a reinforcement learned-based trading bot using the stable-baselines3 library. I've come up with an environment that seems to be able to learn how to make profitable ...
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Positional Encoding of Time-Series features

I’m trying to use a Transformer Encoder I coded with weather feature vectors which are basically 11 features about the weather in the dimension ...
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Pattern recognition for live stream Time serie

I would like to submit you a problem with which I struggle. Suppose I have this kind of record over time in a dataframe: fig.1 If we zoom in a bit we see such shape: fig.2 We see that the general ...
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What could be causing the poor performance (MSE) of a dense neural network on a real time-series dataset?

I am trying to understand time series analysis and actually I am following the course "Sequences, Time Series and Prediction" in Coursera. The course is based on a synthetic dataset, ...
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MLP or RNN for Regression of Smooth Function (No Time Data)?

My Problem consists of Input sequences in the form of $x=[B,z]$ and one output $y_i$ for each data point $x_i=[B,z_i]$. For one sequence/dataset $B$ is a constant, whereas $z$ is continously between 0 ...
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LSTM: Simple value series vs Complex value series

A model for learning a trend graph can be this way: To learn a sequence of N numbers LSTM layer of M units Dense output node of 1 unit The problem is a trend graph in history can be simple: Case 1:...
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Can you train LSTM on a dataset with several separate time-series?

I want to use LSTM in the problem of sports prediction. I know you can use LSTM to predict time-series values such as financial data ... In such time-series each value is part of the same sequence. In ...
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What can be the reasons for validation MSE < training MSE at beginning of training and network failing to generalize afterwards?

I am using a Convolutional Neural Network for regressing time series data. The objective is to predict an obfuscated metric. The training metrics and losses are as follows. The val_loss is lower than ...
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time series analysis: predict number and type of service

I have temporal data regarding the number of customers who requested a specific service in a given period (month and year). Below is a small excerpt from the dataset: Month-year: month and year when ...
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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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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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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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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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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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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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1 answer
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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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3 votes
1 answer
155 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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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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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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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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133 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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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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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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1 vote
1 answer
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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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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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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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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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1 answer
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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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2 answers
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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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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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1 vote
1 answer
261 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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2 votes
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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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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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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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1 answer
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How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have a question I have had a dataset below ...
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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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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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1 vote
1 answer
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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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1 vote
1 answer
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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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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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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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1 vote
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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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1 vote
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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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1 vote
1 answer
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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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2 votes
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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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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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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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