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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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7 views

dataset where the data changes occur irregularly, alternating between every two and three days

I'm working with a dataset where the data changes occur irregularly, alternating between every two and three days. what should i do to generate a corresponding date series to match this irregular ...
2 votes
1 answer
66 views

Relating changes of a value in time to known events

I work with two datasets. The first dataset contains fluor values measured every minute. The second dataset contains certain events and their time. We know that these events cause peaks in fluor ...
1 vote
1 answer
121 views

predicition for a specific month

I am attempting to build a predictive model based on the past historical data. I have details of specific machine failure based on the past year data. I have data from some months of 2016 and from ...
2 votes
2 answers
166 views

How do I use rnn to forecast to n periods with limited data?

So this is my 1st time trying to run a small time-series dataset through an RNN, but after a lot of searching, I haven't been able to find, 1. How I can use this to forecast to n periods ? (like in ...
1 vote
0 answers
20 views

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 ...
1 vote
1 answer
53 views

Formulate multivariate multistep time series forcasting using traditional machine learning, NOT deep learning

How do you represent multivariate multistep data using traditional machine learning? I know this seems like a tailored problem for RNN/LSTM, but I am wondering what the alternative machine learning ...
0 votes
1 answer
130 views

Time series forecasting with non-temporal information (exogenous features)

I'm reviewing many time-series algorithms and libraries, such as Prophet, darts, auto_ts, etc. All libraries discuss univariant time-series (where the task is to forecast based on a single time-series)...
0 votes
1 answer
56 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 ...
3 votes
1 answer
303 views

Data normalization in nonstationary data classification with Learn++.NSE based on MLP

I need to predict technical aggregate condition using vibration monitoring data. We consider this data to be nonstationary i.e. distribution parameters and descriptive statistics are not constant. I ...
2 votes
1 answer
473 views

Batch processing with variable length sequences

I have a lot of time series with different lengths. I would like to know what are the best practices to fit them to a Bidirectional LSTM model. The problem is a Binary Classification of Sequence to ...
2 votes
2 answers
418 views

How to deal with variable length input in the architecture of deep learning methods?

I am working on a variable-length classification problem. I want to utilize multiple Deep learning methods in combination, like CNN, LSTM, attention, etc. Now I'm quite confused and having difficulty ...
0 votes
1 answer
156 views

Training XGBoost on time series features of varying sample length

I have some time series data that contain features that that go back anywhere from 5 to 50 years. I've considered imputation (e.g. taking the mean), but I'm not sure it's feasible to impute such large ...
1 vote
1 answer
19 views

Best metric to assess similarity between flight trajectories features

Consider a flight as represented by a dataframe with spatial (latitude, longitude, altitude) ...
0 votes
1 answer
104 views

Prediction intervals for future timestamps - out-of-sample

I've created a model for out-of-sample forecasting that uses multistep recursive strategy to reduce my problem to regression, the predictions are sufficient but I was wondering if there is any ...
1 vote
1 answer
99 views

Error when checking target: dimensions error in CNN-LSTM model for multivariate time series forecasting

I'm making a CNN-LSTM model to forecast multivariate time series: ...
2 votes
1 answer
25 views

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 ...
3 votes
1 answer
1k views

Whole time column in csv file convert into UTC (epoch) using python

I have a dataset with time and columns. I want to plot a graph with time and value. I tried many methods but didn't come proper graph. Because I have a time series. Then I thought I will convert time ...
1 vote
1 answer
181 views

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 ...
0 votes
2 answers
87 views

Sourcing (discounted) products customers want

Goal: Generate a list of 100 products per vertical (e.g. fashion, electronics) that the teams should source, discount, and list on the website over a specific period. You may assume all customers are ...
1 vote
1 answer
29 views

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 ...
2 votes
2 answers
2k views

Train MLP Neural Network on time series data?

Newbie question here but I was curious to ask if an MLP Neural type network can be trained on time series data? The dataset that I have is an electricity type data set from a building power meter and ...
1 vote
1 answer
58 views

Do I need to standardize time series data in change point detection?

I have process data in time series data(0min, 1min, ... 999min). I don't know what does the variables mean. They are just written in X1, X2, ... X52. Each row means the data at the time. At certain ...
0 votes
2 answers
122 views

Time Series data visualization

When I visualize data using matplotlib it displays very well, but when using Plotly, the data display very bad.
2 votes
0 answers
22 views

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 ...
0 votes
1 answer
25 views

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. ...
1 vote
1 answer
198 views

Fully endogenous models for predicting multivariate time series

I have a formal social science background but I am new to data science. My interest is in building predictive models for applications in the social sciences, mostly (but not only) in economics. I am ...
0 votes
1 answer
57 views

How do I deal with time series data that has overlap with different # of features?

I have two machines, Machine A and Machine B collecting Time Series data. The first machine runs every day and collects 5 features, the second runs every Friday and collects 10 features. Trying to ...
0 votes
1 answer
33 views

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 ...
1 vote
1 answer
89 views

Using sensor data and a know reference point infer the position of a moving robot

Say, the robot is starting at a known position and I've data coming off of the robot as it traverses the grid layout. Exploiting the nuances captured in the data - like the implication of unequal rpm ...
0 votes
1 answer
36 views

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 ...
3 votes
1 answer
80 views

Detect Missing Records in Dataset

I have a dataset that contains several measures from various widgets on a daily basis. While the widgets remain relatively stable over time, sometimes there are legitimate reasons for one to disappear ...
3 votes
1 answer
98 views

What Models should i try for this problem?

I need some advice for a problem i'm working on with automobile data. The vehicles provide a series of codes at every second which are bieng stored, though it can vary how many. For example , at time ...
3 votes
3 answers
2k views

Multi-Source Time Series Data Prediction

I was wondering if anyone has experience with time series prediction for data from multiple sources. So for instance, time series $a,b,..,z$ each have their own shape, some may be correlated with ...
0 votes
0 answers
11 views

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 ...
0 votes
1 answer
81 views

how can we feed both data time series and non time series data together in machine learning classification model

I have a dataset(IoT wearable dataset) composed of time-series and integer data; the objective of my task is to use the dataset for classification. Whilst current libraries in sktime accept ...
4 votes
1 answer
303 views

Clustering time series based on monotonic similarity

Context I am involved in the task of clustering 1500 time series of 500 observations into a few clusters. The time series share all the same observed properties at different spatial locations, but ...
0 votes
1 answer
1k views

How to revert np.log(data) and data.diff()?

I have used np.log(data) and then applied data.diff() to transform my data in timeseries model. I have the predictions. How do I ...
0 votes
1 answer
203 views

Binary Time Series Forecasting

I am working with daily binary time series forecast as follows: The target : purchase decision (0: not purchase, 1 purchase Features: day, weekday, promotion, holiday,.... The objective is trying to ...
1 vote
1 answer
70 views

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 ...
3 votes
1 answer
275 views

time series prediction using arima and non linear trend and too much residuals

I am working on forecasting a financial index, i tried decomposing the time series using : ...
1 vote
1 answer
230 views

Algorithm suggestion for anomaly detection in multivariate time series data

I have time series data containing user actions at certain time intervals eg ...
-1 votes
1 answer
33 views

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 ...
0 votes
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 ...
3 votes
1 answer
84 views

preprocessing time sequence

I have a long list of event (400 unique events, sequence ~10M long). I want to train an RNN to predict next event. The preprocessing steps i took are: (1) turning to OneHotEncoding using pandas: <...
1 vote
1 answer
24 views

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 ...
0 votes
1 answer
943 views

Use both differencing and normalization in time series modeling to make it stationary?

I am working on a time series dataset. Should we use both differencing and normalizing or either of the ones to make it stationary?
2 votes
1 answer
50 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 ...
1 vote
2 answers
162 views

Alternative methods for novelty detection and correlations

Hey mates I have the following project: Imagine having two datasets A and B. Each dataset consits of 101 time series with the ...
0 votes
0 answers
14 views

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 ...
2 votes
1 answer
166 views

Reinforcement Learning on real time data over a web server

Question: is it possible to implement a reinforcement learning model over a NodeJS server? This server would be receiving binary forms of data (open /close; yes/no) in real time. The objective for ...

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