Questions tagged [time-series]

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

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11 views

LSTM autoencoder reconstructs input in ascending order

I implemented an autoencoder LSTM using Keras just as indicated in this article: article. The problem is that the reconstructed input of the time-series is given in ascending order with respect to the ...
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1answer
18 views

Timeseries VAR vs VARMA model: issue in time to fit model

I want to use VARMA model on a data of about 80000 samples with 10 features. I tried using VARMA model from statsmodels with p=50 and q=10 but it is taking too much time to build the model. I tested ...
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Using time series to predict house prices vs. multiple linear regression

Re-post. Machine Learning Courses often teach house prices prediction using multiple linear regression - when we want to predict the value of a variable based on the value of two or more other ...
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Arima model aic continuously decreasing with value of m

In this figure, i am doing time series forecasting with arima model, where it can be clearly seen there is a seasonality of 2 years, so the value of m should be 104 for the better prediction as the ...
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How to forecast timeseries based on different events?

I have a few IoT sensors around my house that over time store some events with timestamps. Each sensor has a unique type e.g. ‘front’ or ‘back’. Let’s call this set X. Now I have one sensor which ...
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Differences between autoencoders and dynamic time warping?

While they work quite differently in terms of implementation, the end result for unsupervised learning is quite similar: Dynamic time warping measures the distance between timeseries-like data (which ...
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1answer
50 views

Extract relevant features from time series data

I have a time series data set from a sensor and the task is to predict the time before a failure event is occurred. The data set has one feature and has almost 20 million rows. This is a regression ...
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10 views

How to make ongoing prediction with panel data with Keras

I have trained a CNN model in Keras for predicting risk of an event happening, where the labels are 0 or 1(one-hot). Input data:...
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1answer
19 views

Correlation between event and time series value recorded different time span! [closed]

I want to test correlation between occupational accident times (occurred from 2010 to 2018 and recorded as time point yy-mm-dd hh:mm) and time series physiological variables (measured from workers for ...
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1answer
20 views

How can you adjust a prediction based on features in the future being different than predicted?

I have a model that takes mostly cumulative data, and makes a prediction. It's not baseball, but I'm using this as a pretty accurate analogy. You put in all the totals so far, and it make a prediction ...
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Is there a method to cluster multivariate time series with differnet number of variables?

Say, there is a dataset of timesries. The majority of timeseries has 4 variables. Occasionally, a timeseries can has a meaningful extra column. So, how to deal with a not constant dimensionality of a ...
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17 views

How to handle time series missing values

I have a database of thermal consumption of 100 buildings. Each file has two columns, one is timestamp and the other is usage. My task is to build a prediction model for forecasting the usage for the ...
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ML methods for prediction, using categorical variables and time

Most of the time series analysis tutorials/textbooks I found time series data, usually deal with continuous numerical variables. I am currently trying to solve a problem that deals with multivariate ...
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19 views

How to setup LSTM problem when using multiple time series inputs?

Problem: Suppose I have 50 time series which I want to train on. For each series, given the last say 4 samples, I would like to predict the next value. Suppose each series is 100 samples. I break ...
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17 views

Sequence classification with highly irregular time series

I'm trying to predict whether a sequence of events contains or will contain a specific event type, with labels being a binary yes or no to the specific event type occurring in that sequence. My data ...
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14 views

How to deal with different length entities in a Keras DataGenerator?

I'm solivng a prediction problem where I need to predict the demand of multiple articles based on their performance during the last 7 days. To get the most out of the data I am trying to implement a ...
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16 views

Time Series Forecasting with RNN/LSTM/NARX

I have some experimental datasets (like 4 or 5), and each dataset has three time series data, say $u1(t)$, $u2(t)$, and $x(t)$. The three time series of each experiment are similar but not the same. ...
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Learnable distance measure for segments of periodic multichannel signal?

We have a set of users, each with several sessions, each session represented as multichannel signal. Users behave differently, but if we visualize several sessions of the same user we can often see a ...
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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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19 views

Are there any methods to detect whole multivariate time-series as anomalous from a set of multivariate time-series?

Consider a scenario with Dataset D as {T1, T2, ..., Tn} and Ti is a multivariate time-series of length mi as {X1, X2, ..., Xmi}. Here each record of the time-series Xi is a vector of attribute values {...
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1answer
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How do I encode time in high dimensional space?

I have a dataset of form text, text, category, category, time, text and I would like to apply the attention mechanism to it. This requires that all inputs be in the ...
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35 views

How to apply a groupby rolling function to create multiple columns in the dataframe

I am setting up a volume profile series over a stock data. I have implemented the market profile code from this github repo and the link to the data is here and the example here. Some Sample of data ...
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Machine Learning Techinques that Automate Fast Fourier Transform

I have a 40k Hz time-series data of vibration, which is used to predict equipment failure. The goal here is to make a system that predicts it automatically. I am thinking of a couple of ways but not ...
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1answer
48 views

Predicting parallel time series with multiple features

I am trying to predict sales for 2 departmental stores which share similar demographic properties. My goal is to make a single LSTM model to predict sales from these parallel time series having ...
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35 views

TensorFlow 2: Find MAE, RMSE for validation dataset in time-series LSTM

TensorFlow 2 "Time series forecasting tutorial" (https://www.tensorflow.org/tutorials/structured_data/time_series#recurrent_neural_network) gives an example of a LSTM multi-step prediction model that ...
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1answer
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partial numerical array - pattern matching

I have a linear numerical array source and I want to find/match test array as pattern : ...
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42 views

Time series data analysis for diffrent time interval

I have 16 datasets with time and one parameter. like: ...
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17 views

how to predict the time series data with two target outputs

I am a newbie to time series problems, I am doing one of the time series problems, I a have 317 columns and, I need to predict the stock Revenue and Price, I am trying to do it, but the many columns ...
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Approach to classify blocks of time series

I am wondering if there exists an approach to classify blocks of time series, and not specifically individual time series. If so, can you point me out papers/articles/tutorials where these type of ...
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15 views

Financial Time Series and Machine Learning question

I am working on a Machine Learning project applied to a financial time series. Initially, I grabbed features (open, high, low, close) and implemented a Random Forest. One of the subsequent tasks ...
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38 views

which algorithms can be used to extrapolate non-linear data?

I have a dataset, where target value changes in time in following way: I need to predict target value for upcoming month, however I struggle to find a method to extrapolate the function that defines ...
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26 views

Will flattening multivariate time series data before clustering make the results meaningless?

I have a large number of financial time series that I wish to do cluster analysis on. Each time series has the same length and spans multiple years of daily data (returns, volatility, etc.). As part ...
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2answers
44 views

How can I approach this problem?

Let's say I have a dataset with pricing information for the same flight during the past year. So, for a flight departing on day D, I have the available price from D-130 up to D (departure day). Then ...
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51 views

Alignment of time series

I have two time series of different lengths: ...
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1answer
17 views

Is it necessary to sort time series data on datetime stamps?

Say I have a time-series data frame for a bank that records number of cases filed for 16 districts as below (cases can be for many purposes - loans, credit cards, real estate, etc.): ...
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1answer
31 views

Using LSTMs for continous learning and predicting

I'm trying to develop a model to predict a commodity price movement direction based on previous observations. The model should learn common technical analysis patterns, e.g. head and shoulders. So, I ...
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1answer
25 views

Synthetic time series generation according to some distribution

I'm trying to develop a change detection model that uses sliding windows. Given a time series with some features I've a sliding widows that analyses that time period and compares with a successive ...
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2answers
99 views

Time Series Model for multiple trends

I am a newbie in the ML field. So please, neglect or better correct, if I am wrong somewhere. Currently working on a model training for time series data. My problem is a little more specific to bike-...
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21 views

How to do Goal Based Time Forecasting?

I have a yearly budget of $10,000 and I have my past monthly expenditure data. Using the past monthly expenditure data, I need to predict the date at which I'm most likely to exhaust my yearly budget. ...
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18 views

Training time-series regression RNN's

I'm looking for references on training time-series regression RNN models. For learning purposes I want to implement myself using autograd (or JAX) rather than a high level library. I cannot find ...
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25 views

Proper loss function for sequence prediction model with multi-step output

Consider a typical time series (sequence) prediction problem that use previous $k$ step historical features to predict the next step target. We use RNN model as an ...
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How to handle data with dependency on two different dates

I am currently dealing with a dataset that contains multiple date-time fields: "buy-date" and "receive-date" which both have an effect on the prices and amount of offers made. One example could be: <...
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1answer
26 views

Time Series Classification for loan data

I have multiple columns for loan installment repayment. As there is a field for month of repayment. I want to predict if the customer is going to pay next month installment or not. As I have ...
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1answer
39 views

Predicting future order dates and order amounts for customers with online retail data

Using the online retail II dataset (https://archive.ics.uci.edu/ml/datasets/Online+Retail+II), I'm trying to predict when each customer will place subsequent orders, and if possible, the monetary ...
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1answer
26 views

How can I use time stamps for classification?

My data consists of entries when an event is True, namely, when a train crossing is down. So I will have entries within a day like so (just examples): ...
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1answer
28 views

R: Error when using Aggregate function to compile monthly means into yearly means

Disclaimer: I'm extremely new to R and have been getting by with using google as my professor. I have a somewhat large collection of monthly values over a period of several years from several ...
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46 views

Setting up RNN in TensorFlow for time series forecast with variable input series lengths

I am building a model with keras for time series prediction. The structure of the problem is as follows: The input is a time series of 5 numeric features The ...
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1answer
32 views

Terminology - regression with one output and multiple output variables

I am trying to predict the response when the input is represented by Fourier transform. These form the features and are typically represented as a vector, $x_1,x_2,...,x_d$ where $d$ is the length of ...
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43 views

Using LSTM to forecast vehicle position - multivariate time series - Matlab

I am trying to train an LSTM model on Matlab to forecast the position of a vehicle when driving around a roundabout. My main concern right now is that my dataset consists of 4 features (X position, Y ...
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Churn rate prediction based on sequencial data

I am trying to build a machine learning model that can predict if a certain user will churn based on its historical static and dynamic data. The data looks like below: ...

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