Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
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Determining increments for aggregated time series data to determine impact of individual features
I'm working with a data source that provides itemised transactions, which I am aggregating into 1 hour blocks to determine a 'rate per hour' as the dependent or target variable - i.e. like a time ...
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Determine the effect on margins of a price increase
I hope you can help guide me in the right direction! Any advice is appreciated!
Situation
I'm currently analyzing the effect of a price increase from a retailer on a few 100 products. I'm interested ...
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Time Series Data Noise Handling Questions
There are manufacturing time series data as shown in the picture.
The average of the two variables is about 100.
However, the noise value is 6500 and 65000, which is too different from other values.
I ...
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multi dimensional time series and matrix profile method
I have a time series of the following format:
...
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Temperature lag forecasting
I am working on a data science project on an industrial machine. This machine has two heating infrastructures. (fuel and electricity). It uses these two heatings at the same time, and I am trying to ...
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Time series classification but with a sequence in output
I'm using Python and I have a training set of sequences of this shape: (None, 9, 25),
where:
9 are rows representing years from 2012 to 2020
25 are features.
So each of this 25 features has a value ...
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Calculate features on stationary time-series data
I am trying to create a deep learning model that predicts the future price of crypto currencies based on past data.
I downloaded the Open, High, Low, Close and Volume (OHLCV) data from yahoo finance ...
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classify blocks of time series of unknown section lengths basis slope and smoothed diff
What will be the best approach to classify blocks of a univariate time series that contains data of a fuel tank level (data captured every 30 seconds)? The slope of the curve would be an important ...
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Which dataset for multivariate time series forecasting
I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ?
Can I use a dataset ...
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Time Series Clustering on sales data -- any ideas?
I have a retail store dataset, and I am interested to do some time series clustering on this data, what idea you find interesting for this purpose?
I have so far:
What sales trends there are across ...
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Make fitted xgboost or linear regression model predicts values in thé future
I have a machine learning model that I fitted with xgboost and linear regression. My dataset has thirteen features and has price as the target.
Is there any way to ...
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Is it impossible to predict defects with data that are not labeled?
There is manufacturing data with 10 process variables. Normal and bad labeling are not done. It's tabular fdata.
Do you have a paper that only uses data that are not labeled to predict defects or to ...
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Predicting a signal based on other signals
I want to predict a signal based on other related signals, how would I go about doing this?
My current approach is to do some feature extraction (in the time and frequency domain) on both the ground ...
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ML methods for vector correlation
I am dealing with a timeseries consisting of input flow sampled every 5 minutes over 441 days. My aim is to find any possible correlation from data coming from:
The same day of the week
The same ...
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Multi-Label time-series classification with LSTM: large performance decrease for longer periods
I have daily data on event occurences, so for each day I have a vector like [1, 0, 1] indicating that on this day event one and three occured, but event two did not occur. I want to train a model to ...
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how to align sliding window to extract features from multi modal timeseries data?
I have two datasets that are collected at different frequencies at the same time. One is recorded at 128Hz and another one is recorded at 512 Hz. I am trying to extract some features using the moving ...
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How to do multivariate time series classification using C# and either Accord.NET, Encog or ML.NET?
I have a time series based on financial security prices with additional features. I wish to feed this series into some ML construct in order to perform multi-class classification.
Most of the ...
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Are residuals in time series decomposition normally distributed?
Does residuals in time series decomposition have to be normally distributed ?
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RNN/LSTM timeseries, with fixed attributes per run
I have a multivariate time series of weather date: temperature, humidity and wind strength ($x_{c,t},y_{c,t},z_{c,t}$ respectively). I have this data for a dozen different cities ($c\in {c_1,c_2,...,...
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Proper iteration over time series data for LSTM neural network
I’m using the supervised learning method with an LSTM network to predict forex prices. To achieve this I’m using deeplearning4j library but I doubt several points of my implementation.
I turned off ...
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PyTorch: LSTM training loss not decreasing; starting at very high loss
I am training an LSTM to give counts of the number of items in buckets. There are 252 buckets. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning ...
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P value calculation for stacked LSTM model for binary classification
I need to calculate the P-value for a stacked LSTM model. Does that mean that I need to run the model multiple times and consider the null hypothesis that the model's accuracy is almost the same ...
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Time series classification using multiples multivariate multi-length timeseries
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I would like to develop a time series classification algorithm to classify use a of parachute.
My data consist of multiple recording files (around 5min at 100hz, length of the recording vary) with a ...
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How to constrain a dataframe to specific date range?
I have a dataframe that I would like to chunck- or rather run temporal sentiment analysis at different times. I am trying to measure how sentiment changes as part of user identity in extremist social ...
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Interval segmentation of time series data
I have this attached time series signal (its actually from an electrostatic sensor, everytime someone walks or moves, I can see that in the signal).
For the machine learning part, I would like to get ...
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Spike and dip cleaning in Big Query ML ARIMA_PLUS
If there is sales of a product spiking once every year (let us say every year in May). Will the ARIMA_PLUS model of Bigquery clean every year May sales as spike ...
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multi variate time forecasting
I want to forecast in a time serie the 'output'. I have from the past the correlated time series 'output', 'capacity' and 'load'. I also know from the nearby future the time series from the 'capacity' ...
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Performance metrics for LSTM Autoencoder
I am building an LSTM Autoencoder (unsupervised model) to detect anomalies in a time series dataset. The input is telemetry data from routers and I want to detect anomalies in the throughout of router....
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Problem with timestamp
I have a data set with 2 timestamps (1 hour and then 15 minutes). how can I standardize the timestamps as 15 minutes? is it a practical practice to add other 3 rows (each one is 15 minutes) with the ...
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Missclassfication of Hand Generated Signals
I have two types of time series accelerometer data from two kinds of machines - one is very fast (Type A) and another one is relatively slow in terms of number of peaks/movement (Type B).
I have tried ...
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Stride in time series classification/regression using neural networks
When dealing with time series in neural networks, we use windows with a size and a stride as input. Is it advantageous to train such a neural network with a stride that is smaller than the stride used ...
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Is there a way to forecast a time series multiple linear regression using externally made dummy variables?
This question concerns question 4h of this textbook exercise. It asks to make future predictions based on a chosen TSLM model which involves an endogenously (if i'm using this right) made dummy ...
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Negative forecasts in ARIMA
Can ARIMA (specifically BigQueryML ARIMA_PLUS) give negative forecasts even if the training data has only 0 or positive values?
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Many regression lines in a plot
How do you plot many regression lines in a plot? This concerns the textbook question from "Forecasting: Principles and Practice". A dataset concerns winning times of Olympic running events. ...
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Removing seasonality in time series forecasting
In time series forecasting we are removing the "seasonal" component to fit models better and have better forecasting. But why? if I should give an extreme example: if I have a sin wave, I ...
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Why fourier transform extrapolation goes to extreme on edges but not in the middle, how to fix it
Why fourier transform extrapolation goes to extreme on edges but not in the middle, how to fix it
with python
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Is there an ai to create more samples of an instrument from a single sample?
I don't know if there is a term for this.
Is there an ai to create more samples from a single sample?
a sample in music is a sound that has a unique pitch.
for example .sf2 files used in music ...
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Can preprocessing in time-series data (e.g. deseasonaliation or detrending) helps create better forecasting model?
I am reading a paper that mentions the following.
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Plot multiple time series from single dataframe
I have a dataframe with multiple time series and columns with labels. My goal is to plot all time series in a single plot, where the labels should be used in the legend of the plot. The important ...
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Multi-level timeseries forecasting? How to do it?
So, I just finished a 48 hr datathon, and I did terribly, to be honest. It was my first datathon.
We were given a list of datasets:
5 months of taxi demand data (January to May)
Weather dataset
Zone ...
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How can we predict a value after several rows of data?
I have a regression problem in which for each week I have several rows (variable between rows i.e 1 week might have 1800 rows and other might have 5000 rows).
My target is to predict a value at end of ...
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Clustering of time series data
I have a time series data set. I want to use Dynamic time warping for distance measurement. For algorithm, I was thinking of using either K-means DTW Barycenter Averaging (DBA) or K-medoids. Data has ...
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LSTM Auto-encoder Implementation
I'm trying to implement an LSTM auto-encoder in PyTorch for time-series data (univariate and/or multi-variate). Initially, I assumed it would be fairly easy but I realised there are a few ...
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Multiple Timeseries Anomaly Detection - identifying which feature is anomalous?
I have a multivariate time series where I have features such as:
temperature set point
energy used
relative humidity, etc.
Currently, I'm creating univariate anomaly detection models in Python using ...
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How to perform Timeseries Forecasting on dataset with repeating dates?
I have a dataset, I have to perform timeseries forecasting on that dataset. The dataset has date column, the dates in date column are duplicated. We have 5 classes, a date will have a sales record for ...
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Normalization and Denormalization
I have few queries. 1) Is normalization required for ANN / CNN /LSTM ? 2) If we normalize the data with MinMax Scaler, then in that case how to denormalize it and when to denormalize it so that we ...
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Autocorrelation Chart showing different colours and no Shadow for checking the Orders to be used for ARIMA
I have been doing a project on Time Series data and one of the items was to test for AutoCorrelation and found the charts in a different color than what I expected. I also don't see the shaded part ...
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One anomaly detection model for all industries
Background - I'm creating a time-series anomaly detection (TSAD) model for the wifi throughput. My customers are 2 banks, 5 retail stores, 4 universities, 6 hospitals. Currently, I have 2 options to ...
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Anomaly detection and root cause analysis
ARIMA is widely used for anomaly detection on time-series data e.g. stock price prediction. ARIMA assumes that future value of a variable (stock price in our case) is dependent on its previous values. ...
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Understanding of spikes and comparing different time-series data
Complete newbie here so, do forgive any misunderstandings that I may have.
Currently, we have a lot of metrics to track each detail of our backend applications like traffic, response times, memory ...
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