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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What should you consider when splitting timeseries data into train and test sets?

My goal is to classify windows of multivariate timeseries data into a positive and a negative class. When I construct sliding time windows and randomly shuffle them into the train and test sets, I get ...
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What are alternatives to PCA for time series data?

I have some data (20 stock price time series) and want to compare different approaches for dimensionality reduction other than PCA (I want to fit only 2 variables in my AR model). I've tried ...
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Why use auto-regressive models for time-series?

This is a naive question... But I realized that auto-regressive predictions can be inherently unstable due to previous prediction error monotonically accumulating in the inputs: $M(h_{t-n},...,h_{t-m},...
profPlum's user avatar
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Realtime Inference of Stateful Variable-Length Time Series LSTM - Getting around Sequence Length vs Inference Frequency Tradeoff

I am trying to train a neural net for low-latency signal filtering; I've been recommended to use a stateful LSTM architecture for this task, however, having corrected some , it seems to me that trying ...
stellarpower's user avatar
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Difficulty with 'group_by' in Polars

I am new to polars, trying to convert my pandas code to polars. The below code is in pandas, (book --> pd.DataFrame) ...
Sarvagya Porwal's user avatar
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Optimal number of epochs for Transformer network on time series data?

I have a transformer network that is trained on time series data. The task is to predict if a variable will increase a certain percentage in the next dt days. The input is data from the 90 previous ...
QCQCQC's user avatar
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When are Transformers better than LSTMs in time-series tasks such as classification?

I’m working on a time-series classification problem and trying to decide whether to use a Transformer or an LSTM. From what I’ve learned, Transformers are better suited for capturing long-range ...
Mark Cortejo's user avatar
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Is there any advantage to providing multi-dimensional input to torch modules?

Most layer types in torch.nn such as torch.nn.Linear accept input with more than one dimension. Is there any advantage in doing so if you can shape your data to represent a certain arrangement in ...
kot's user avatar
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Improving forecast for LSTM with additional data

There are two timeseries X and Y. The timeseries X spans the duration [1 Jan, 20 Jan ] and the Y spans [1 Jan, 25 Jan].  I am interested in timeseries forecasting of variable X for the duration [21 ...
pkj's user avatar
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Simultaneous forecasting and classification

I'm working on a project where I need to perform both forecasting (regression) and classification using time series data. The dataset is labelled. I've been exploring LSTM networks due to their ...
mike7's user avatar
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How to extract features from patterns in time series data

I have a time series welding data I wanted to create a model which can predict some weld parameters but extracting those parameters from time series data is being so difficult. Currently I tried ...
THUNDER 07's user avatar
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How is the accuracy as a metric in a Keras machine learning model calculated? Is it a valuable metric for LSTM

I'm training a LSTM neural network for time series prediction in Keras. During the training of the model, the loss (mse) gradually decreases each epoch, but the accuracy as well as the validation ...
Mappy's user avatar
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Is my 1D signal using CNN & RNN regression reasonable?

I want to know if my impact-echo signals are proper with CNN or RNN regression model. I got some simulated signal, as following shows. In previous research, people mostly consider frequency or even ...
hui30319's user avatar
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RNN - Time To Stop extimation given non-categorical events sequence

I have a dataset which contains sequences of event of the following type: Timestamp_s Timestamp_e Event_type I've preprocessed the data to create sequences of the shape [seq_size, unique_codes_count]...
GPU'njoyer's user avatar
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Backpropagation in a transformer

I have a transformer for timeseries forcasting based on this article https://arxiv.org/abs/2001.08317 Given a source containing $src=(x_{t-5},x_{t-4},x_{t-3},x_{t-2},x_{t-1})$ and a target of $tgt=(x_{...
Michał Kuczynski's user avatar
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Multi Output Regression but num of objects to predict vary per sample

so recently I came across a problem of predicting the positions of objects from a pulse wave. My biggest concern here is that for each data sample, the number of objects varies. I know that this ...
mim96's user avatar
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How do I find a similar RNN as a starting point?

I am new to machine learning and neural networks and I want to create a neural network for a study project. I would like to create a RNN, that uses one (A) or several time series (with the same length,...
xlaub's user avatar
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LSTM Ensemble: Combine low, mid, and high frequency time series data

I am trying to implement time series classification, but I am struggling a bit with the fact that my multivariate data has mixed frequencies. I have about 10 variables that are updated every minute, ...
Ai4l2s's user avatar
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Regarding the use of Time2Vec as positional encoding for Timeseries Transformer

Although the transformer architecture was originally designed for NLP, there exists several articles and papers that attempt to apply the same architecture for numerical timeseries classification. ...
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How to classify positional time series data?

I am working with a data set that includes (x, y, z) coordinates and timestamps of human movement. The input data looks something like: ...
Jason Shaev's user avatar
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Can I combined the trained model between different source but same model structure?

Here I got two different deep-learning models that were trained by LSTM and time-series data. The data is the usage percentage of CPU from two different computers. Each computer job was the same. It ...
orde.r's user avatar
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What are the most common fault prediction algorithms?

I have to predict a fault (automotive related) as much in advance as possible. Right now I have found a solution that is somewhat satisfactory (a good number of true positives and a low number of ...
Pigna's user avatar
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Early binary classification of timeseries

I'm trying to figure out how to solve this problem that I'll try to explain in the next few lines. I have a timeseries of length ~200k values and every 700 points I have a label that indicates the ...
irazza's user avatar
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How can I transform a signal into another using supervised learning?

I'm trying to transform a signal into another using supervised learning. My main goal is to create a model capable to transform a raw signal (Blue Line) into something similar to the "ideal" ...
Nathaldien's user avatar
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Which preprocessing is the correct way to forecast time-series data using LSTM?

I just started to study time-series forecasting using RNN. I have a few months of time series data that was an hour unit. The data is a kind of percentage value of my little experiment and I would ...
orde.r's user avatar
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How to extract body of a base-model and fine tune with that body on different shape dataset like this situation

In BERT like transformer model (I am not talking about BERT in this thread), it has 2 training objectives Masked Language Modeling and Next sentence prediction right? and BERT model is also supports ...
Arjun Reddy's user avatar
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Should I downsample because of overrepresentation of geographic locations in time series data

I am in the start of working with a project where I am hoping to be able to classify activities based time series data. I have historic data; lat/long/speed/(..) as well as the activity. The challenge ...
bjornasm's user avatar
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How can one incorporate spatial correlations into time series forecasting?

I am working on a project, where I am trying to predict temperatures of various streets and I have their locations recorded. I was wondering if I could somehow train a model that could incorporate ...
user380572's user avatar
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35 views

How is batch data processed in a 1D convolution layer?

Suppose I have a time series data written in a matrix $\mathbf{X} \in \mathbb{R}^{N \times d}$. The sequence length is $N$ and $d$ is the number of features (I have $d$ series). Say I have a batch of $...
poglhar's user avatar
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Feature Extraction for timeseries temperature signal [closed]

i have two temperature signals from which one is sensitive toward a specific event. I would like to know what other features can be useful to extract apart from: Angles (between the two). Slopes ( ...
Mirza's user avatar
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Creating a Dataset from Time Series Data

Context I'd like to build a regression model for this data to predict a user's test scores given their study habits. Basically, the variables are in two separate csv tables similar to the ones below. ...
LittleLulatsch's user avatar
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1 answer
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Regression Model diverging after adding a new feature with higher variance and magnitude

In a time series regression problem I'm predicting "change" rather than the actual intended value i.e Instead of: ...
Darren Rahnemoon's user avatar
2 votes
1 answer
68 views

How to fine tune model on dataset which is having different shape compared to dataset model is trained on

I am having 2 questions as follows: I am using normal CNN model for time series classification, Where my dataset1 is of shape (28,9,1) and I trained my CNN model on dataset1, where now the input ...
Arjun Reddy's user avatar
1 vote
1 answer
66 views

Transfer Learning for Solar Energy Production Forecasting with LSTM: Generalized vs. Specialized Models

I am working on a solar energy production forecasting problem using LSTM multi-step models to predict 1/4/8h ahead of solar energy production for different solar installations. Our goal is to help ...
Guilherme Vieira's user avatar
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1 answer
852 views

Difference between sequence length and hidden size in LSTM

It does not come clear to me how the seq_length is not the exact same as the hidden_size in LSTMs. For example, in the next ...
moth123's user avatar
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Missing Value Imputation for Time Series

I am working on a Stock Price Forecasting project, the data of which consists of 5306 instance & 12 columns. Of these, 2 columns contain about 500 instances of missing values (the starting 500 ...
sidhant3070's user avatar
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Preventing Overfitting While Cross Validating Time Series Models

I have some time series data (e.g. daily rainfall for 10 years) and I am interested in fitting a time series model to this data and record the error. I want to use the "rolling window cross ...
Uk rain troll's user avatar
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Healthcare alerting system based on weather data?

I have a database containing time series information about the temperature, pressure, humidity, wind speed and the number of people visiting my hospital each day. I want to design a system that ...
Agatino Giuliano Mirabella's user avatar
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1 answer
58 views

Are there public examples of AI models that predicted short-term price well?

This question is inspired by Is there any AI model that predicts short-term stock price well?. As answered, game theoretically either nobody would reveal such a model or it would have been alread ...
Rexcirus's user avatar
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What reinforcement learning algorithm should I use for the following problem?

Environment I have a static timeseries environment meaning the environment is the same. This problem is a multi armed bandit problem. Time t0 t1 t2 State s0 s1 s2 Score 10 0.1 0.2 Class 1 0 0 ...
adamwest's user avatar
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122 views

Is there any AI model that predicts short-term stock price well?

One simple way of predicating short-term stock price is to simply take the average price of the previous few days. I wonder if there is some AI model/methods which can do better than this using only ...
faceclean's user avatar
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Regressing parameters that map between two curves machine learning

I am wondering if anyone has experience in regressing out parameters that map one curve to another. For example, I have two curves that look like this. I used some non-linear equation to map orange to ...
user2551700's user avatar
1 vote
1 answer
39 views

Many To One LSTM - Can I Use the Same Sequence as Input from Previous Timesteps?

I'm new to LSTMs, and I'm trying to do a basic timeseries prediction using stock prices. However, I'm a bit confused as to how the LSTM is supposed to remember outputs from previous timesteps when it ...
Krusty the Clown's user avatar
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SCINet: how does interactive learning work?

i'm having some trouble understanding how does the basic building block of a SCINet works. In the paper the author describes the SCI-block with the following figure: In which $\phi$, $\theta$, $\eta$ ...
Juan Hirschmann's user avatar
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2 answers
390 views

Should I use multi-armed-bandits or RL for a financial time-series problem?

If we take simple financial timeseries data(stock/commodity/currency prices), State(t+1) does not depend on the action that we choose to take at State(t) as in Maze or Chess problem. Simple example: ...
kobo's user avatar
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1 answer
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Should we always use the usual no leakage train-val-test splt in time series?

Some of you may be familiar with the unusual split scheme used for time-series data. In short, there is a saying that one should only consider a split where the training set comes prior to the testing ...
Hadar Sharvit's user avatar
1 vote
1 answer
62 views

What type of neural network architecture allows filtering out of unwanted sounds?

I have a use case where I will be inputting audio to a model, and the output of the model will be the same audio except with certain sounds removed (volume set to zero). The dataset is generated by ...
HonestMath's user avatar
-1 votes
1 answer
163 views

Generating synthetic time series data with limited data

I would like some opinions on my current situation. I have a set of time series data that I want to forecast. The data however is not very long (around 500 rows) so I was looking into generating many ...
codinator's user avatar
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1 answer
137 views

Can i train xgboost on multiple time series csv files at the same time?

I built an xgboost model to predict stock it now trains on 1 stock at a time its a csv file I use pandas to load it. Is there a way to train the model on multiple stocks at the same time? What would ...
AJB's user avatar
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Very high ACC (ca. 95%) with 1DConvNet for Time Series

Does this sound legit, for people working with CNN and Time Series? I have a Framework that applies Dynamic Tim Warping (DTW) on time series, using the DTW distance matrix, I cluster my data and ...
Skobo Do's user avatar