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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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balancing and imbalancing in supervised anomaly detection probelm

I am dealing with a supervised anomaly detection problem, where I have labels with 0 for normal and 1 for abnormal. The default distribution of the dataset is highly imbalanced with a ratio of 96:4 ...
Amir's user avatar
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Why the graph doesn't display the missing value even though values in data file are not missing?

i'm estimating a GARCH model , modeling the volatility in Bitcoin price and expected inflation, the graph is log diff of expected inflation proxy "market yield on treasury securities " Thank ...
REDA OUMLIL's user avatar
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Time series windowing approach

I have this problem statement from my project is that base on the 5 mins data to predict the next 2 minutes. (you can look at the top) each segment of 5 minutes predict the next 2 minutes. However, is ...
curiosityfrown's user avatar
2 votes
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How to analyze time series data and create time series model in Python?

I am trying to understand time-series data and model. In youtube tutorial and others, mostly univariate examples are shown. And they are applicable or suitable for those conditions. What if our ...
Bad Coder's user avatar
1 vote
1 answer
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Multivariate Time series forecast deep learning

My Dataset: I have data for vehicles - mainly engine sensor data but also gps location, weather etc. The data is high frequency - every second. I have aggregated to 1 minute. I roughly have somewhere ...
Joshua's user avatar
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How to properly select features for time series ML models

I've been trying to get good references on how to solve a problem that's been bothering me regarding the modelling techniques I've used. I'm currently interested in making forecasts using ML for ...
loguimaraes's user avatar
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How to explain missing dates to a model?

I have this dataset that I'm trying to train a neural network on. The problem is that since weekend dates are not available, I am not confident in whether the model is able to account for that. ...
Akshat Vats's user avatar
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Why does forecasting with an LSTM yield better results with shuffling?

I first partition the timeseries data into train, validation, and test splits, without performing any shuffling. Each row is a window of ordered samples, so my training data might be shaped ...
MuhammedYunus's user avatar
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Can anyone help me understand this problem in my data?

I tried making a model using the autoTS library but the thing is in the result it gives me the following results. I checked everything there is no missing data but the original data had a missing ...
theunknown's user avatar
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Should you seasonally decompose TS data before linear regression?

I want to apply the U-MIDAS method which is basically Least Square regression to a cross sectioned time series. Do I need to seasonally decompose my X and Y and should I test for unit root? Some of ...
J_Bake's user avatar
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Optimized input data structure for ML model training

I have a large dataset (20M+ rows) of user interactions which I want to use to predict the probability of a customer purchasing an item in one-, three- and six months time. However since the ...
MJ_VdH's user avatar
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1 vote
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SARIMAX Non-Time Series External Variables

I'm new to time series forecasting and I'm currently reading a book about it. As I'm learning about the SARIMAX model it mentions the external variables being time-series as well. Do the external ...
avb0101's user avatar
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I need suggestion for a project

I want to make a forecasting system which will forecast how much quantity will be sold next year based on the previous 5 years' data from 2019 to 2023 and want to predict for future years. Now the ...
theunknown's user avatar
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Whats a suitable feature selection method for Time series data across multiple files?

My problem is basically a higher dimensional regression, where my input is (100 levels, 300 timesteps, 23 features) My goal is to build a deep learning LSTM model that finds which level the data ...
Youssef Badr's user avatar
1 vote
1 answer
20 views

Best metric to assess similarity between flight trajectories features

Consider a flight as represented by a dataframe with spatial (latitude, longitude, altitude) ...
Droid's user avatar
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How to perform extrinsic regression on variable-length time series

I have a signal that describes the the flow in a water pipe. Assuming that individual water consumptions have constant flow rate, each water consumption could also be described by its starting time, ...
broidul's user avatar
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Should I standardise time series data for deep learning classification?

Say I have time series data for classifying stars using deep learning based on stellar variability, with each time series data measuring the flux of the star overtime. For each star, I have the data ...
Johnathon Smith's user avatar
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facebook prophet interaction terms

I have a univariate time series data on an hourly basis of patient census. I ran an ARIMA model with interactions on daily and weekly seasonality (periods t=24 and t=168 respectively) and found the ...
mathcomp guy's user avatar
2 votes
1 answer
51 views

Beginner Question on ARIMA

I have started learning time series forecasting and struggling a bit with the concept of differencing, particularly for (S)ARIMA(X) model, which is often recommended model to start with. I am trying ...
miroslaavi's user avatar
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Positional Encoding for FFNN?

Here is my problem: I have input [x1,..,xt,n1,..,nt,1,2,...,t] where there is a missing timestep xi, and I use neighboring time series (found with KNN) n1,...,nt to add more features, as well as time ...
Michel Hijazin's user avatar
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pattern within autocorrelation confidence intervals

I have a time-series producing the following auto & partial correlation plots. What insights can we make when there are oscillatory patterns within the grey region of insignificance? Does it make ...
eliangius's user avatar
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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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31 views

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 ...
reuseman's user avatar
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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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1 answer
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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 ...
J_Bake's user avatar
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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
1 vote
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25 views

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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7 views

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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23 views

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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35 views

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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1 answer
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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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1 answer
58 views

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
2 votes
1 answer
53 views

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
2 votes
1 answer
69 views

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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1 answer
14 views

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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12 views

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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21 views

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.
L1rola's user avatar
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0 answers
19 views

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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1 vote
1 answer
57 views

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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0 answers
48 views

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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15 views

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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