Questions tagged [time-series]

Time series are data observed over time (either in continuous time or at discrete time periods).

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times series prediction with several regressors( using R)

Absolute beginner here. I'm trying to use a neural network to predict price of a product that's being shipped while using temperature, deaths during a pandemic, rain volume, and a column of 0 and 1's (...
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Should I use an LSTM model when the outcome is a different variable from the training data?

I am trying to model a health outcome as a function of climate variables. I have many observations of health outcomes at different times and locations (but NOT a sequence at one location). For each ...
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What is the right way to make prediction with time series forecasting models?

I'm into ML and data science for a while but now I started exploring time-series forecasting, and I have (lets say) a simple question: What are the features/inputs for the time-series forecasting ...
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Prediction method when the time series is not sequential?

I have multivariate time series data consisting of monthly sales of contraceptives at various delivery sites in a certain country, between January 2016 and June 2019. The data looks as follows: The ...
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1answer
9 views

determining size of batch, time of sending and memory in to send from scala to ML section

I have a time series (sampling time: 66.66 micro second, number of samples/sampling time=151), I would like to determine some anomalies in them, the inputs are made by scala customer. would like to ...
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Loss drops to NaN after a short time for a time series classification

here is my model code for a binary classification of a time series: ...
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Selecting the best model parameters from grid search SARIMA [Time series]

I ran a manual gridsearch of SARIMA across several parameters and now I have 7875 rows of scores (RMSE, MAE, MAPE each) from it. These were the parameters (30k+ permutations) I ran a grid search over- ...
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Recurrent models for asynchronous / mixed frequency time series

What are some of the RNN/LSTM models for handling mixed frequency/asynchronous time series data, such as macroeconomics, financial, precipitation, etc.? So far I have found phased lstm from a similar ...
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How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have had a dataset below: sensor1 sensor2 sensor3 ... 2021-01-01 1.32 2.2 1.0 2021-01-02 4.3 2.0 0.8 ... ... I know ...
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9 views

LSTM decoder with 2d's input

I am developing a CNN-LSTM autoencoder in pytorch to predict time sequences. The CNN input is a RGB image: RGB image => tensor[Batch size= 4, channel = 3,width= 256, height=256] and the output is ...
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Var_Imp Algorithms in Pred/Class Problems: Can I use it in TS Problems?

OBJECTIVE OF THIS POST: Solve a query about the possibility of use prediction/classification variable importance tools in a time series type dataframe. Collect the largest number of variable ...
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How to improve geom_line plot in R: linetype and x-axis detail to show each year

Context: I'm attemping to plot time series data in R with geom_line using the package ggplot using the code below. See png file for outcome: ...
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LSTM Timeseries Forecast with long-term, variable forecast horizon

In my graduation project, I use sensors to collect power usage data for home appliances with 5 minutes intervals, I want to create an ML model that takes in a variable number of values (len(dataset)) ...
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Prediction Intervals on (Multi-Step) Judgement Forecasts

Are there any R packages available or general methodologies for calculating prediction intervals on judgment-based forecasts? I've looked at Hyndman's text and the R forecast package - which will ...
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11 views

identifying time series with threshold breach potential

(moved from stackoverflow.com) Hi all, I'm trying to solve a following problem. I have a set of various devices feeding their readings into a system where they are stored as time series: timestamp, ...
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Forecast methodology for geographic variables that are somewhat related

I'm creating time series forecasts for different geographies and wanted an expert opinion on how I can take into account geographic relationship to improve my model. Is there an algorithm that's ...
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Data-driven industrial kiln drying of wood [closed]

As part of my project, I was commissioned to develop an algorithm capable of optimizing the operation of a wood dryer based on input data (including temperature, relative humidity, wood moisture ...
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13 views

Multi-step time series prediction using multivariate input to get multivariate output

I am experienced in ML for tabular data but new to time series, so I am hoping to frame my question properly. I have this data series in this format: t a b c d e f 0 1 2 3 4 5 6 The columns ...
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Conditional variational autoencoder: Feeding labeled MNIST to encoder with Keras

I am looking for a code implementation of a CVAE using MNIST in Keras. I found this Youtube video: https://youtu.be/8wrLjnQ7EWQ that does VAE, but I am not sure how do I convert this and make encoder ...
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Can we add positional encoding to time series input for time series prediction?

I want to use classical machine learning models such XGBoost for my time series prediction. Since the input data for XGBoost/sklearn based models is 2d i.e. (n_samples, n_features), I want to encode ...
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19 views

Getting KeyError while executing forcast( ) in statsmodel's holtwinters function

I'm trying to get time series prediction using the following code. ...
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13 views

How to use one hot encoding with time series data ( arima eg)

I have cumulative number of medical cases weekly for 60 weeks and categorical data on week wise events that occurred. I’m trying to analyse which event may increase or decrease the cumulative cases. <...
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185 views

What is the best way to train a model?

I am trying to train my model for sports predictions. The data frame is as a below given example: ...
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57 views

1D CNN time series classiifcation : ValueError: Shapes (10, 10, 8) and (10, 8) are incompatible

I'm working on a time series classification using ASHRAE RP-1043 chiller dataset which has 65 columns and more than 3000 rows for each chiller fault and normal condition. And I have used 1D CNN and ...
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1answer
35 views

modeling time series data with large number of variables

I want to model time series data of 52 dependent variable using neural networks in order to forecast these series in future . I have tried some architectures of LSTM and CNN (conv1D) models but my ...
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1answer
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Time Series Target variable taken at much lower sample rate than input features

I have a regression problem that involves predicting a patient's blood pressure from a range of vital sign readings including PTT, PPG, and HR. Each of these input features has been taken at the same ...
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9 views

Implementing mean shift clustering in spatio-temporal domain?

We used meanshift clustering in the spatio-temporal domain (i.e., [x, y, t] with a kernel of size [32, 32, 200]). We treat clusters with at least 2 samples as fixations and use cluster center as the ...
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35 views

LSTM Many to one with multiple time steps for time series (multi class classification)

I want to do a time series multi-class classification for fault detection and diagnosis with time-series sensor data set which contains a sequence of 50 records of normal data and sequences of another ...
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1answer
24 views

Should I shuffle my `train_test_split` if my time series contains lagged features?

I understand that it is not recommended to shuffle your training and test sets for time series, else the model will not be able to understand the time dependency of the features. However, I am now ...
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9 views

how do I Analyse effectiveness of signals generated through a forecast model for financial instruments? [closed]

We are given a dataset which is generated by a forecasting model along with historical data for some financial instruments. We need to analyze the effectiveness of the signals generated using various ...
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21 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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1answer
18 views

Choosing a Change Point Detection Algorithm

I am currently working on a dataset that belongs to the restaurant and food delivery domain. After completing sentiment analysis and quantification, I now need to select a Change Point Detection ...
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11 views

How can i improve my Bidirectional LSTM timeseries forecasting

I am trying to forecast a timeseries and I am using LSTM for it. But the forecast outside train data is pretty bad. I tried adding layers, changing epochs but couldn't improve. The forecast is ...
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1answer
26 views

Evaluation metric for imbalanced data

Hi I'm a CS graduate student I have a question for AI or data experts. I'm writing a paper My dataset is time-series sensor data and anomaly (positive class) ratio is between 5% and 6% you can see the ...
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3answers
40 views

How to use machine learning to find pattern of similar regions in signals

I have a long time series signal. This signal is usually very stable, but it will change when the sensor is stimulated, and this change is usually very short. I know this can be trained using the ...
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21 views

Cross correlation

I am trying to find a good algo (low latency) that is able to take two time series and determine which one is leading on the other one if any. The time series do not necessarily have the same ...
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1answer
13 views

How to compare error metrics for model with and without Seasonality?

I am aiming to guage the difference in my model performance from using data with and without Sesonality removal. My approach to Seasonality removal is taking the log of the column data and then ...
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Using STL(Seasonal-Trend decomposition using LOESS) for Anomaly detection

I am using STL to decompose my time series data in Season, trend and residual and then by applying this(see below) on residual. I am detecting the anomaly ...
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MY lstm has a really low accuracy, is there anyway to improve it?

I am trying to make a model to classify whether these patients can be diagnosed with dementia by their 35 days of biometric data. A brief summary of a dataset is below. as an input X_train data, it ...
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12 views

Prediciting outperformance - choice of statistical design?

I want to predict relative outperformance between a stock and an associated benchmark index using time-series models (e.g. ARIMA, LSTM) and some exogenous variables (day of the week, corporate ...
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1answer
26 views

How to test unsupervised learning methods for anomaly detection?

How to test unsupervised learning methods for anomaly detection? I am looking for a test strategy to evaluate my result of my anomaly detection technique? what is your offer more than evaluate with ...
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13 views

Sarimax forecast : How to properly deal with non working days

(asked first on stackoverflow but felt like it would be smarter to put it here) I'm trying to build a Sarima model to predict day by day the expected value of several measures (separately), the point ...
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1answer
26 views

ML model to forecast time series data

This question has three sub-parts, answering each of which probably doesn't require huge text. I hope that is okay. I'm trying to understand time series prediction using ML. I have the target variable ...
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27 views

Panel data classification and regression?

I am very new to time series/panel/longituginal data. From my understanding Panel data = multi-object time series and I have some panel data in long format (objects have multiple rows corresponding to ...
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9 views

Tsallis entropy - advice needed regarding obtaining probability distribution

As is always the way I stumbled across Tsallis entropy on SO whilst looking for something completely different. This soon lead me reading all sorts of interesting but terse academic papers. I am ...
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16 views

What to do when facing bias in LSTM?

I have created an LSTM model which is trained on an 8-hour time frame for a cryptocurrency. When the training is finished I see that it is learning the pattern but there is some bias in it. How to ...
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2answers
28 views

XgBoost given targets its only feature but fails when test targets are outside the range of training targets?

I'm learning to use XgBoost, and I'm doing an exercise involving predicting prices. However I'm noticing some weird behavior where XgBoost's predictions deviate from the target value even if I'm ...
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1answer
23 views

Evaluation metric for time-series anomaly detection

I have a question for AI or data experts. I'm writing a paper My dataset are time-series sensor data and anomaly ratio is between 5% and 6% 1. For time-series anomaly detection evaluation, which one ...
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Static ML model or Time-Series? How to model/predict a binary target when I have time variant features but most features are constant?

I have been working with Real World data from patients. I have a dataset with information about 10million patients; Collected over a span of varying duration (5 to 20 years). What I am predicting is ...
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Applying Differencing on a time series, before or after train and test split?

I am attempting to improve my RNN model by making my dependent variable, a stock price, non-stationary. I am aiming to make the series stationary by removing the trend with a log transformation and ...

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