Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
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What is the minimum requirement for the dataset for time series forecasting?
I have a dataset of patients, and for each patient a measurement is taken 3 times per day. For example, Patient 1 has recordings at 7.30 am, 12.30 pm and 8.30 pm. Patient 1 has a collection of 30 days ...
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10 views
Excel date-time auto fill error
I have data of 10 metrics for a 24 hour period which is in 5 minute intervals. I merged all the 10 data metrics into one file and converted it into a .xlsx file.
After opening the excel file I ...
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6 views
The optimal number of segments for Piecewise Aggregate Approximation (PAA)
I have time series dataset and I want to break it into segments, in order to run autoencoder on individual segments. After some research, PAA does what I need: it breaks the dataset into segments (...
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20 views
Multiple Event Identification in a Series (Segmentation or Event Extraction)
I've just started a project and I've having trouble even defining the problem! I'll try now and any help, no matter how small will be appreciated greatly.
Scenario: Given a series (doesn't matter if ...
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15 views
How to compare methods for forecast of time series where the element of the series are vectors?
Suppose we have a series of vectors v1, v2,...,vn and we must forecast vn+1. We must confront various methods of forecasting (VAR,LSTM ecc). How can we choose a set of metrics in order to perform the ...
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1answer
16 views
Time Series Classification for 1 hour blocks
I am doing some analysis on time series.
The time series would consist of 3 channels and contain 5 minute interval data.
What I want is to be able to give it a 1 hour block of 5 minute interval data ...
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9 views
Removing seasonality in time series by differencing a weighted average of the previous period
I have effectively removed weekly/daily seasonality from my time series by subtracting the same time interval a week before. This makes the series mostly stationary, which has been great for modeling ...
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25 views
Reshape Time series data for Conv2d Block
I am modelling my time series data into a supervised learning problem for the input to a conv2d block in pytorch from this tutorial.
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2answers
42 views
How to handle weekdays in a NN?
I want to test if using additional information of weekdays would improve my NN. Therefore, I just converted the weekdays numerically such as
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2answers
33 views
Pandas datetime error when reading from excel file
I am trying to read an excel file that has two columns using pandas.
This is how the data looks in excel file:
...
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0answers
36 views
Forecasting future close returns
I have end-of-day time-series dataset with following data
Ticker Date Open High Low Close Volume
I have calculated end-of-day closing returns ...
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2answers
41 views
Understandable and explainable machine learning model
I want to find formula for best financial portfolio.
Inputs: Historical fundamental data for last 15 years. For 3000 companies for every quatal we have things like ...
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1answer
67 views
How to update the posterior belief when we are observing a stream of correlated data from a fixed but unknown data source
I want to build a [probabilistic] model that aims to infer the true value of an unknown categorical variable, $y \in \{1,2,..., K\}$.
We have a dataset $(X,y): \mathbb{R}^d\rightarrow \{1,2,..., K\}$ ...
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15 views
Can I use/modify an Autoencoder to handle missing data?
I am about to implement an Autoencoder to detect anomalies. Therefore, e.g., in my test set, there is a situation where the data stream broke for some days. This results in a lack of data and should ...
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3answers
40 views
+50
Detect geolocation match a GeoJson pattern
I'm trying to detect if a geolocation (lat, lng) match a GeoJson pattern. As example i have line of location points and i want to detect if a new point can match that pattern in certain radius, like ...
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0answers
21 views
Data Augmentation techniques for classification of imbalanced time series datasets
Now I have a task to classify the imbalanced time series datasets using ML classifiers, such as Logistic Regression, Decision Tree, SVM, and KNN. I am not allowed to use the Deep Learning tools, such ...
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0answers
14 views
Characterizing different parts of a time series data set
I generate time series data sets that often looks something like this:
Given the series data (blue), I am trying to automate placement of "red" and "orange" levels to be similar to what is shown: ...
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0answers
11 views
Smoothing price according to sales volume
I was looking at the evolution of price for different products and I observed the following graphic
As you can see the orange time series has a sudden change at the beginning of 2019.
Therefore I ...
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0answers
27 views
Time series forecasting produce same values with different training data
I'm developing a python program which predict daily timeseries values.
Each daily timeseries contains 288 values (a record every 5 minutes).
The main idea is to train a LSTM model with 7 days data ...
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0answers
19 views
How to label time series data correctly for training RNN/CNN models?
My Case
I want to tackle a deep learning classification task using various smartphone sensor data. I will use a self-built data acquisition app and basically walk around with the phone, manually ...
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0answers
7 views
models and algorithms to measure the difference and trend of a matrix sequence
I have a matrix sequence, i.e, a sequence consists of about 5000 matrix, each matrix are of the same dimension, e.g., 25*20. Are there any models/metrics to characterize the trend of these matrix. For ...
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0answers
18 views
Dropout noise shape when applying it on a series
I am training a neural network based on the Deep Sets framework (https://arxiv.org/abs/1703.06114, https://arxiv.org/abs/1810.05165). The basis of this approach is that one has a series of input ...
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1answer
29 views
How do I assess whether two time series change together?
In this example, at timepoint 5, both signals move up together. I would like to quantify these similar movements, and ideally disregard the parts where the signals are almost constant.
What ...
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2answers
131 views
Time series forecasting dilemma. Could feature engineering overcome time dependency?
I keep reading articles about time series forecasting.
They all start from the same assumption: time series forecasting can't be treated as a regression/classification problem. It is time dependent, ...
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1answer
33 views
Anomaly detection thresholds issue
I'm working on an anomaly detection development in Python.
More in details, I need to analysed timeseries in order to check if anomalies are present.
An anomalous value is typically a peak, so a ...
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0answers
15 views
Statistic that is responsive to changes in time series, yet not too volatile
Let's say I have a fairly volatile time series $X_t$ - it doesn't have any reason to show an upward / downward trend, but it does show drops and spikes from time to time. It can also change level (e.g....
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0answers
17 views
How to approach mapping families of vectors on a lattice and forecast resulting value
I describe here a model to describe how neighbours influence a node.
I wish to implement it to attempt forecasting to values associate nodes;
I post here asking for suggestions on mathematical model ...
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0answers
17 views
LSTM pulse signal prediction
i'm trying to capture long-term dependencies using LSTM, by creating a unit pulse signal every 62 points.
The idea is to go back 62 time-steps and copy the value for the next time-step, so as to ...
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0answers
25 views
Does the add_regressor method on Facebook Prophet also work with categorical variables?
I went through the documentation of Facebook Prophet and was able to build a similar model for my time series dataset. The additional regressors I used were numeric. I achieved a reasonable MAPE score....
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1answer
19 views
Are time series algorithms immune towards collinearity?
I have a time series dataset with 63 features and a univariate dependent variable. This is my first major time series project, so I was wondering if algorithms like ARIMA and LSTM are immune towards ...
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1answer
19 views
hierarchical clustering doesn't work as expected
I have a precomputed distance matrix.
I'm trying to do an hierarchical clustering using scipy:
...
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0answers
14 views
Temporal outlier Analysis on sensor data
I am working to find anomaly/outliers in sensor data using unsupervised machine learning (without training dataset). I have around 20000 samples taken per minute of various sensors. I just need to ...
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0answers
19 views
Any previous work to develop time series models for multiple label sets with different sampling rates
We have time series data with multiple labels: one label is sampled every day and the other label is added every one month and we would like to train the model accounting for both labels at the same ...
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2answers
34 views
Is autocorrelation the same as multicollinearity?
I have a time series dataset that has 63 features with traffic_volume as the target.
...
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0answers
5 views
Time Series and forecasting individual reservations
What kind of algorithm would best for following problem.
I try to forecast reservation of different kind of tables. Let's say
I have 100 different tables, which are reserved for from 17.00-22.00
...
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0answers
8 views
Accelerometer and Gyroscope features
I am having accelerometer and gyroscope reading along x,y,z axis and want to get motion direction info at each time step. What all feature extraction would be best suited for this type of requirement. ...
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0answers
10 views
Generating correlated time series with different probability distributions
I have the following problem to solve: I need to generate two time series, each modeling a variable (lets call them VarA and VarB). For each of these variables, I have a PDF (PdfA, PdfB) which belongs ...
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0answers
13 views
Choosing a distance metric and a clustering algorithm for time series
For every entity I have a corresponding time series which is built by a sliding window (win_size=7d, win_shift=3d, so we have overlapped windows)
With every win-shift, we count how many users are ...
0
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1answer
20 views
Is it common to add noise to Time Series data before training a model
I once read about somebody who added noise to their time series before training a model. They didn't write why they did it though.
Is this common practice?
If it is, why do people do it ie. to ...
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1answer
24 views
Time Series segmentation
I have a time series graph that is segmented into a few parts based on the maintenance day. You can think of it like vertical lines appearing out of the x axis which symbolize maintenance at the date. ...
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0answers
17 views
How to generate sports tracking data using deep learning?
Data: I have a 2D Numpy array that contains tracking data for football. Each row has the (x,y) coordinates for all players + the ball. That's 22 players and 1 ball = 46 columns. The frequency is 0.1 ...
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1answer
28 views
Predicting churn - deal with missing dates in time series and improve modelling result
This is the follow up question for General approach on time series for customer retention/churn in retail.
I have a time series of data in the following form:
...
0
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1answer
37 views
Getting monthly revenue predictions for outlets
I am often presented with a task of predicting monthly revenues of retail outlets. Say I have a training set of N outlets, each associated with a series of historical monthly revenues (target) and a ...
0
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1answer
23 views
Forecasting Consumption for Multiple Products for Multiple Regions
Came across a very interesting Real-World Time Series Forecast Problem.
Can you please help me understand the right track to resolve the below Time Series problem:
Input Data Sample:
and we want to ...
2
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2answers
83 views
Determining if a time series is random
An example time series would be the stock market, which is sometimes described as a random walk. Over time, this is clearly not the case as it has essentially gone in one direction (up) with only ...
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1answer
41 views
General approach on time series for customer retention/churn in retail
I have a time series of data in the following form:
...
1
vote
1answer
20 views
Split time series by python or by keras?
In Python you can use TimeSeriessplit() to split a time series properly for training but you can also do the same(?) in Keras by TimeseriesGenerator.
Which one is recommendable? And/or what are ...
0
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1answer
11 views
Cluster evolution over time
I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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0answers
9 views
LSTM based anomaly detection scheme too closely tracking long spans of anomalous points
I've built a time series anomaly detection process that accurately predicts the value at the next interval. However, when there are dozens of anomalous events in a row, the model starts to "catch up" ...
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0answers
62 views
Building a speech commands dataset for audio recognition applications
I'm working on a DL project to recognize (10 - 15) Arabic speech commands from a continuous stream of audio, and I want to create a dataset similar to Google's Speech Commands dataset.
Fortunately, I ...
