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Questions tagged [time-series]

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

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How do I give weight to recent time points when predicting another closeby time point?

I am building a normal feed-forward neural network to predict the value of a masked time point using regression, e.g. I have values for x at times 1, 2, and 4, and I want to predict its value at time ...
Michel Hijazin's user avatar
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Forecasting Resource Depletion in a Distributed System

I manage a distributed system where each node contains six interchangeable resource slots, sourced from a diverse pool of resource types. Each type has a finite number of units, which get consumed ...
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How to work with temperature data

So I have some equipment temperature and i have outside temperature (both are collected daily) and I want to predict the equipment temperature. However, I'm new to this and unsure about which model to ...
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Efficient anomaly detection in unordered market data - is it possible?

I'm a little bit stuck on how to efficiently model anomaly detection for the following problem, probably because of my lack of experience with time series modelling: I retrieve market data sorted by ...
Skyence's user avatar
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Does nowcasting use cross sectional data?

So in recent months I have been reading about nowcasting. From what I understand what UMIDAS does is that it transforms the dataset into cross sectional data and then runs OLS. The more I read ...
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How to check if an event affects time series

We have time series data. Depended variable – interest rates, about 15 years, monthly data. Independent variable – event, rating announcement (rating may change or may not), happens 2-3 times per year,...
NoobinStatistics's user avatar
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How to compute confidence interval xgboost regressor?

I have time series data to predict values for the next 6 months. I have an xgboost model that predicts the six individual months, for the business what is important is that the cumulative value of ...
tailsrockc's user avatar
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Do categorical embeddings leak data in time series?

I am a bit confused on this matter, I can't find any resources that touch on the following but my logic says that embeddings do introduce data leakage in time series: Considering a temporal dataset ...
idontknowmuch's user avatar
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Uncertainty in stacked ensemble model

I am using the stacked generalization scheme to combine the predictions from different machine learning models (input models from now on). I am currently calculating the prediction interval for each ...
umbe1987's user avatar
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How to select the subset which can represent the whole dataset

I have a time series of data for the whole year, which I need to run the analysis on Python. However, it takes a long time to run the model. I want to select a subset of data that can represent the ...
Thành Trần's user avatar
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The best order for analysis steps in building econometric model with time series linear regression

I am working on a project whose goal is to build a linear regression model for a time series dataset. I was provided with a blueprint of all required analysis steps. This led me to wonder what is the ...
Brzoskwinia's user avatar
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How to calculate rolling standard deviation in 1 hour intervals across dates?

I have time series data of electricity consumption. I want to calculate rolling standard deviation of 1 hour intervals with window size of 10 i.e. I want to take values from 8-9AM for last 10 days and ...
Yogesh's user avatar
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Machine learning model that takes multiple records as input to help predict the last

I want to create a ML model that is able to forecast the yield from a farm. My data source gives me data about the inspections from the field, but that is too much info to fit in 1 record, so there ...
Milan N's user avatar
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deep learning for stock prediction

I am learning deep learning . Right now I am using MNIST data set, which contains tens of thousands of scanned images of handwritten digits, together with their correct classifications. My question ...
quanity's user avatar
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Uneven spaced time series data, any advise on how to approach?

In dealing with uneven spaced time series data, any advise what would be the approach ? data is ECG data to predict if the blood pressure Sys would drop -20% or 80% of normal. In the usual approach ...
curiosityfrown's user avatar
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Lost when trying to get good time series prediction results (regression problem) even after trying many things

I'm not able to get good results after a long time testing when using TensorFlow to predict time series data (regression problem). I don't know if the problem is with the data (little quantity and/or ...
Marco's user avatar
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Pearson correlation with overlapping data

I have a financial time series and I want to calculate correlation between past and future returns. First I select look back and holding periods, say l and h respectively. Then I calculate past ...
970541804's user avatar
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Univariate time series forecasting with bimodal distribution

This is my first ML project ever. Well the objective is to build a forecasting model of a univariate time series containing solid waste weights loaded from the city of Austin,Texas. The distribution ...
karim abousselham's user avatar
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Time Series Analysis and Price Elasticity

Introduction: As of now, I am a fourth year data science student. As of now, I also have my own company where I work parttime (8/12 hours per week) to gain some more experience in the domain. As you ...
Martijn's user avatar
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input problems of using LSTM in python to forecast future value

There are two columns rainfall data and water level in my dataset and I want to predict the water level based of the past values using LSTM on python. My problem is do I need to include the past ...
user161683's user avatar
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Temporal mismatch

I am building a predictive model to determine risk for a disease over the course of a hospital stay. I am using medical records from a hospital electronic medical record database. The predictions are ...
healthydata's user avatar
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How to do Time Series Forecast for data appearing on the same day for different Fiscal Years

I've been trying to figure out a solution to this problem for the past couple of weeks and after all my efforts I realized this is a very niche problem. I'm trying to forecast data for event ...
Minhaz Khan's user avatar
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Train-test split strategies in sensor time series

i'd like to train a supervised machine learning algorithm on my sensor data (Accelerometer XYZ). I've already segmented the data with a sliding window approach (1s window_size, 50% overlap) and ...
André S's user avatar
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Model Architecture for Time-Series Forecasting with Categorical and Multivariate Data

Context: I was looking at using an LSTM model to forecast the amount of gold gained for each of 10 heroes in a game of Dota 2, a MOBA game, as a base model in some type of model architecture. The game ...
DCRA's user avatar
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What is the best library for time-series analysis?

Recently, I've been using statsmodels, but I would like to know if others work fine for you.
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What is the advantage of positional encoding over using additional features?

Popular models such as the transformer model use positional encoding on existing feature dimensions. Why is this preferred over adding more features to the feature dimension of the tensor which can ...
kot's user avatar
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Creating a rule based approach to identify Stock Out products

I am trying to build a Stock Out Predictive Model based on input variables such as: Sell In Unit: Units per month sold to wholesaler by manufacturer Sell Out Unit: Units per month sold by wholesaler ...
Sushmoy Mallik's user avatar
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LSTM Model for Multivariate Multi-Series

I'm looking to create an LSTM model to predict a certain label trained on multiple short-time series data. How would I go about doing this? Each time series has 10-30 time steps and 20 different ...
Chino's user avatar
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Time-series forecasting analysis

I'm currently doing a time-series forecasting project for the agriculture sector. Basically i'm trying to make predictions about fruit future prices. I've been doing well so far, but now I'm stuck. I ...
L1rola's user avatar
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Weighting training instances by time in machine learning models

I am training a neural network based on data whose relevance I think diminishes based on how far each instance is in the past. I've had a look and one way to do this it seems is to 'weight' training ...
joe_credit's user avatar
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Addressing prolonged high matrix profile values in anomaly detection

In an anomaly detection task, I have a data stream where each new data point is generated every 5 minutes. When a new data point arrives, I compute the matrix profile using Stumpy's stumpi function. ...
Vlad's user avatar
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Imputing techniques for missing values

Currently, I am working on a project for which I would appreciate some feedback and opinions. My dataset contains data about daily solar radiation from 8 stations and covers the period from 2017 to ...
Fatima's user avatar
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Which model should I select for a Multivariate Time Series interpolation where I have covariates and missing dates?

I have data with the sales of stores across a two year timespan. Sample data: Date Target Store Sales Target Store Promotion Sales_Store1 Promotion_Store1 Sales_Store2 Promotion_Store1 2022-06-27 30....
Notorious's user avatar
1 vote
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Weird Behavour in Skforcast

I applied XGBoost to forcast a univariante time series dataset, the first time I created my own lags features manually: ...
Bouabdallah khaled's user avatar
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Can I use TimeDistributed layer for multiclass classification?

I have timeseries machine sensor data and I would like to predict when the machine fails using the sensor data. There are 4 Failure states and 1 Normal state, total of 5 classes. I am trying to solve ...
Rushabh Kheni's user avatar
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is Sequences, Time Series and Prediction course up to date?

Recently I asked a question regarding Time series prediction and someone commented that I should consider taking DeepLearning.AI course: Sequences, Time Series and Prediction. My goal is to make good ...
Cohensius's user avatar
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Decomposition and scaling and their effect on interpretability?

I have time series GDP growth rate data that I use as my Y and other X variables that I put into neural networks to make predictions. The two questions that I have are: When I decompose my GDP_growth ...
J_Bake's user avatar
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1 vote
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Misaligned Multivariate Time Series (weather and soil moisture) in soil moisture forecasting

I'm working on soil moisture forecasting using multivariate time series (MTS). More precisely, each time sample comes with multiple measurements regarding: the soil (e.g. soil temperature, and soil ...
Marco DC's user avatar
3 votes
2 answers
127 views

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 ...
Mario's user avatar
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Time Series forecasting with SVR

I am trying to forecast my data by Support Vector Regressor, Here is my code: ...
Hadis's user avatar
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21 views

How do you find the effects of external shocks in stock time series?

I have to identify 4 different points in time per stock price. I have several thousand stock prices, which are available as a monthly time series, so I need an automated calculation of the points in ...
Sepp's user avatar
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1 answer
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Why is my LSTM model not predicting well when predicting labels for a new dataset?

I have a 15 timeseries datasets with 25-30 columns and is labeled by following a complex formula applied on the 25-30 columns. When training, I split the datasets as training datasets and unseen ...
Rushabh Kheni's user avatar
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How to correctly use gstar package of R for spatio-tempral analysis?

I want to perform a spatio-temporal analysis by highlighting spatial as well as temporal dependencies of the data (I have a 'weight matrix' highlighting spatial dependencies of the counties) on the ...
Shashank Gupta's user avatar
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53 views

IoU metric for multi class image segmentation task

My input shape is of (168,18). I create batches of size 256 and create my dataset using timeseries_from_Array_dataset. I am visualizing this 2D snapshot of a multivariate timeseries (batch size- 256, ...
Vjs's user avatar
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1 vote
1 answer
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Multivariate time series forecasting - model selection

I have historical data on customer contracts. I know the date a customer terminated their contract and the date they notified of this termination. For example, a customer could end their contract in ...
BenBernke's user avatar
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2 answers
39 views

Time Series forecasting feature creation/engineering

I'm new to time-series-forecasting and was wondering, whether in a single variable forecast e.g.: X -> Y the creation of additional features of X leads to an improvement when training. So if adding ...
user159972's user avatar
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sampling points on an interpolated curve

I have this interpolated curve (black) using black data points. I used scipy.interpolate.griddata to obtain the curve, I was wondering if there's a way to sample function values from this ...
user159958's user avatar
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1 answer
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How to get the closest samples to time series dataset?

I have a deep learning time series classification model. I want to understand if the model failed to classify, due to missing or incorrupt training inputs. For simplicity let's say we have a training ...
user3668129's user avatar
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1 answer
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Should I choose an ARIMA model (2,1,1) with a higher AIC value or an ARIMA model (6,1,8) with a lower AIC value?

I am trying to fit an ARIMA model to time series data. When I fit the model using auto.arima function in R, ...
Mehmet Yildirim's user avatar
1 vote
0 answers
53 views

How can I use Time-GPT for pretraining my model

I am mentioning Time-GPT here as a placeholder example. It can be any pretrained model. Suppose I have a dataset that requires some time series prediction. How can I leverage a well-trained model and ...
Mohammad Mosiur's user avatar

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