Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
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LSTM for time series forcasting
I manipulate the time series using the different structures of the neural networks in order to make a prediction, and I wonder if there is a way to choose the parameters of the networks intelligently? ...
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How to predict NaN (missing values) of a dataframe using ARIMA in Python?
I have a dataframe df_train of shape (11808, 1) that looks as follows:
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How bootstrapping works for prediction intervals?
I'm experimenting with prediction interval (PI) over univariant time-data using skforecast pythonic package..
in the documentation it is mentioned that:
Prediction intervals
A prediction interval ...
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Time series forecasting: prediction and forecast far from the reality
Apologies for the awkward title, but I hope to be able to regain your confidence.
Let's start with the final output I got, so at least you can understand why I'm not happy/concerned about the outcome....
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Difference between sequence length and batch size in time series forecasting
I am using Keras for time series forecasting and I am trying to understand the tutorial on the offical site of keras about time series forecasting that you can find here (https://keras.io/examples/...
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Time series - is it necessary to retrain the model when new time series data is present
Say you're building a sales prediction model to predict tomorrow's sales value, as well as the next 2 weeks of daily sales. The model is being trained using daily data for the previous 1.5 years, and ...
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Prediction on timeseries data using tensorflow
I have an input and output of below format:
(X) = [[ 0 1 2]
[ 1 2 3]]
y = [ 3 4 ]
It's timeseries data. The task is to predict the next number....
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How to feed a table per timestamp to LSTM neural network?
I have a time-series dataframe like this
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How to calculate the elapsed time of a flag status per day?
I'd like to figure out the elapsed time between flag status changes.
Simplified example: a person can only be sad or happy. I'd like to know how long each mood was active until it changed.
I'm ...
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Predict how many days late or early someone will finish their work
So I have a set of deadlines and people, with a database of when those people finished their previous work and how much after the deadline it was, as well as when the work was given. The work itself ...
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How to reshape data for LSTM training in multivariate sequence prediction
I want to build an LSTM model for customer behaviour. It's the first time for me working on a timeseries, so some concepts are not clear to me at all.
My prediction problem is multidimensional, ...
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Recommended model for univariate or multivariate multistep ahead time series forecasting
I have a dataset consisting of recurring and non-recurring expense transactions from bank accounts, as well as other features describing the bank account and each transation. I aggregate these ...
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Binary classification model with time series as variables
This is probably a simple question. Assume I'm interested in modelling a binary variable, with various covariates, including ones that are time series observations. In the usual modelling approach, ...
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How to optimally train deep learning model using output as new input
I'm trying to train a network to predict the future. My current setup uses 5 time steps as inputs from the past, each consisting of 10 features, resulting in a [5, 10] input matrix (initially ...
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How to use TimeDistributed fo CNN+LSTM?
I am trying to classify 6 classes time-frequency domain signal (STFT spectrogram) with a size of 3601x217 pixels. Assume that for each classes have 70 training samples, 20 validation samples, and 10 ...
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How create a representative small subset from a huge dataset, for local development?
I have a time series problem and the dataset I'm using is rather huge. Around 100GB. For local development I'm trying to subset this into a very small batch around 50MB, just to make sure unit tests ...
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Can I create a layer with multiple rnn cell ? [question about a paper]
I am trying to implement https://dl.acm.org/doi/pdf/10.1145/3269206.3271794 .
Structure:
As it said:
In particular, we integrate the embedding vectors learned from each
individual recurrent encoder ...
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How to combine data from multiple Google Trends queries effectively?
As you might know, Google Trends works by normalising a random sample of the search term data, with the sample changing at least once per day, from my experience. This is not an issue for western ...
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Appropriate Supervised Machine Learning Algorithm for Time series prediction
I am looking forward to the correct ML/algorithm approach for the below issue.
My target here is to predict the target day of the incoming time series below for a new time series. Also below you can ...
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Price Predition for Irregular spaced historic data of non independent Prices
I am a little unsure how to proceed. I am not an expert but on a decent intermediate level when it comes to regular Timeseries.
Now i am faced with a problem that first seemed related, but is an ...
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CNN regression. help to improve current model [closed]
I have time series grey scale images that show movement of fluid with different densities.
I want to predict a pixel value for time t, with (t-3),(t-2),(t-1) 2D images as inputs.
I am figuring out how ...
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Which One 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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Pros and cons of pandas or R for longitudinal data?
Note: I believe this question is not off-topic because it meets all of the criteria for subjective questions that are allowed. I would be happy to rephrase or clarify if others disagree
I'm about to ...
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General approach on time series for customer retention/churn in retail
I have a time series of data in the following form:
...
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Forecasting time series outside the training/test set
I am trying to predict some time series based on precedent values using LSTM.
I have pretty good results when I compare the predicted time series with the test set (0,18% error)
I just miss how to ...
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Non-parametric regression on set of time series: One model for each or one for all series?
Let's say I have a set of 1D time series which values have been samples in equip-distant time steps with timestamps $1,2,3,...$, they have all the same lengths and are somewhat similar in shape. I ...
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Anomaly detection in Time Series Data - Help Required [closed]
I am looking for algorithms on Anomaly detection for time series data. It is uni-variate analysis, considering single parameter (inlet pressure) of air compressor sensor data. The objective is to ...
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Time series data and ML - separating training/test data
I am using XGBoost to try to predict the direction of the stock market based on social media sentiment. Having read through some studies, I was planning to separate the training/test data by time ...
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Bayesian optimization with Keras tuner for time series
Goal: trying to use walk-forward validation strategy with keras tuner for time series when training a neural network (mainly LSTM and/or CNN).
Did anyone find a direct way of doing this?
One ...
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What is the preferred approach for this problem?
I have the Data of 10,000 users Time Session in a website/App, The Login time, logout time, the person activity,
The Data is available for 60 days ( per user )
Using this 60 days data for 10k ...
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Multiclass Classification on live sensor data
I want to use an accelerometer to detect which way a train is heading.
You could do this by just setting thresholds manually to detect the direction of acceleration on the x-axis (which would be ...
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2
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172
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Visualizing Time Series Data
I hope this question isn't out of place here.
I have some time series data from a Zooz power plug that I'd like to visualize. Excel works fine to a point, with small sets of data, but it's not ...
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What is the difference between lookback period and transform a time series dataset into a supervised learning dataset for time-series forecasting?
Let's say I have dataset within the following pandas dataframe format with a non-standard timestamp column without datetime format as follows:
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Problems to understand how to create the input data for time series forecasting with a recurrent neural network in Keras
I just started to use recurrent neural networks (RNN) with Keras for time-series forecasting and I found this tutorial Forecasting with RNN. I have difficulties understanding how to build the training ...
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Applying SMOTE on time series data
I have a dataset that consist of student grades and it's based on a time series. I used LSTM to predict the student future grade. Can I apply SMOTE on this dataset to ensure that the model will not be ...
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Check if change in time serie influence change in another time serie
I have two time series representing scores, lets call it score A - score B,
score A is related to a company and it is observed every year from 1990 (about 27 observation)
score B is related to a ...
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1
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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 message bus.
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Convert time series data to supervised learning problem
I have a similar dataset like the one below. Each row represents a person and there are 3 different variables m1,m2,m3 with 3 measurements each.
I am trying to frame this time series problem as a ...
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1
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170
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Time series forecast for everyday for till a distant future
I have time-series data for every single day from the last 5 years with seasonal variation and a general increase in trend. This is what my data looks like:
And I am trying to predict for every ...
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AR coefficients are not stationary
I have a timeseries data and I want to forecast it by applying ARIMA. After reading data, I decomposed it to analyze its components and get an idea whether it is stationary or not. It seems there is ...
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