Data Science

Questions tagged [time-series]

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

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characteristics of time-series datasets

I am working on two different datasets, one of them is a simulated dataset and the other is a real-world dataset. My understanding of RNNs was that they work well with sequential data where a ...
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I have time series data for months i need to forecast for next 12 months

I have graph like this for that data. When I make adfuller test: ...
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Multiclass Regression for density prediction

This is my first question in the DS-community, so I'm happily willing to accept any kind of (meta-) advice :). I have time-series data for a set of users (~100), ...
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Sequential Learning- How to feed Data?

I have trained neural network model with batch data. Now I want to make predictions using this model, but data for the prediction will be sequential. Is it something should have been taken care in ...
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Changing Timestamp format for Date-Time in Excel/Pandas/Python?

I have a excel data with time stamp format like this "2019-06-10T14:05:00+05:30" [YYYY-MM-DDTHH:MM:SS], I want it to be converted into "2019-06-10 14:05" [YYYY-MM-DD HH:MM], Is it possible to convert ...
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Predicting when component will fail having its parameters data

I have a component and I need to predict when it will wear out and will need replacement. I monitor, let's say 5 parameters of this component, each one is monitored for every run cycle. So, the ...
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Algorithm selection

I have a dataset as below: ...
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Is it valid to shuffle time-series data for a prediction task?

I have a time-series dataset that records some participants' daily features from wearable sensors and their daily mood status. The goal is to use one day's daily features and predict the next day's ...
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How to predict next visit date based on this data

I have a dataset shown below. Here, status is if visit has been done or not and schedule is if next_action_scheduled. ...
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How to write inputs append with time using panda in python

I have a data csv file include with three inputs temperature, humidity and wind. So in this csv file first input recorded at 6:00:00a.m. But I want to start my time as 0 and then my second time is <...
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How can Time Series Analysis be done with Categorical Variables

Most of the time series analysis tutorials/textbooks I've read about, be they for univariate or multivariate time series data, usually deal with continuous numerical variables. I currently have a ...
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Error propagation in Time series forecast with many-to-many multi-steps RNN/LSTM

I am trying to do a many-to-many time series forecast, which features an encoder-decoder model to predict with variable input and fixed prediction period. In my case, I want to predict for the future ...
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preprocessing : Predicting with Multiple+Multivariate+Multitrend time series data

I am trying to predict the value of a variable in a multivariate time series; of which I have multiple time datasets (one system = one dataset containing 10 variables in time and average 120,000 rows) ...
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Error decompose time series

I have a time series which represents the amount of a certain product sold throughout the year 2018. I am trying to decompose the time series but I get the following error ...
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I have hourly data of a metric for 15 days, Can i predict the outcome values for same metric for the next 15 days?

I have tried a linear regression model for the same data, Since the regression line is continuous i'm not sure if it works to predict the outcome values for next 15 days, or for a given period of time!...
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how to write start_time= 0 and increment with 60 minutes to display y value (predict) continously using python

Here I have a dataset with three inputs, temperature, humidity, wind. I want to predict temperature value using these three inputs data in future at every 60 mins. For the time code, here my first ...
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Stationary time series for clustering algorithms

I have a set of time series data that I would like to feed into a clustering algorithm (like k-means, using dynamic time warping as the distance function). After standardizing the data with mean 0 and ...
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Variable length, variable sampling time-series data classification?

I am a bit new to classification and ML. I have a dataset that I'm not sure how to handle in regards to the sampling rate of the sensors I am working with, and the time length that each 'event' lasts. ...
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Auto.arima with xreg in R, restriction on forecast periods

I am using the forecast package and implement auto.arima with xreg. Here I want to forecast ...
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LSTM with fuzzy logic

I need to implement a fuzzy LSTM model for single time series prediction but i am stuck on the algorithm . could you please clarify it . i wanna open a discussion here . There is no Package on python ...
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How to shape input for multi-site multivariate time-series forecasting in LSTM?

Can anyone help me with how to shape the input for multi-site multivariate time-series? My dataset is something like: One csv file for each time-step: Each file contains 5 parameters (x,y, u-velocity,...
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Log and differented Arima model does not fit to actual data (prediction)

I am trying to built a data model with Knime, where I use functions in python for data wrangling and the metanodes (java/R) in Knime for forecasting. Untill now I discovered that I do not have ...
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How to find vertical clusters in 1-D data

I have residuals of a multivariate time series data obtained from sensors on a server.spikes in the plots of residuals indicate abnormal server state. I want to cluster the data into vertical clusters ...
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Multi strep time series forecasting using daily data & LSTM

I have daily data from Jul 2017-Dec 2018 which makes 549 data points. I was trying to forecast Jan 2019 using Oct 2018-Dec 2018. In this example that is demonstrated above I can understand that in ...
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Is there any time series model which handles data at variable frequencies.?

Goal: Predict the yellow points.(yellow events appear at varying frequencies) But I'm struggling to find a good model to fit this use case. Most of the time series algorithms are handling data which ...
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1answer
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How to reach time points clustering?

What I met a problem is I do time-series clustering, and I found the clustering result isn't ideal. I can't use elbow method to know what clustering result is good, that means I have no ways to watch ...
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Finding relative performance of products during certain hours?

Let's say I'm running a bar and I want to know what drinks sell better than normal during happy hour or during certain events. Is there a name for this problem? I'm sure this is a common problem, but ...
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How to test dev set on Time Series data via forecasting

I'm implementing $3$ Bayesian Deep Learning models (links below) for my masters. I'm supposed to test them on a civil engineering time series data. My models ...
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Time Series data: How to convert it in a streaming data?

I have a time series data which is available in offline csv format. I am using this data to create anomaly detection model. Although I could create this model to predict anomalies in this dataset, I ...
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How to apply rolling volume profile function with a pandas TimeDelta

I am setting up a volume profile series over a stock data. I have implemented the market profile code from this github repo. Here the author gets slice the index and apply the main function by ...
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Training and validation loss completely overlap in a time series regression

The dataset here is about the arrival rate in a queue. I used one-hot coding to represent the features for the user type, year and hour of arrival in integer_hour column. Before performing any coding, ...
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Timeseries prediction error measurement. How to deal with diffrent time scales?

I have some time series and a prediction model. Now I would like to measure how good/bad the prediction is for different products. The problem is that for each product the time points (frequency of ...
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Help understanding if suffering from Validation Bias

The goal is to forecast the volume a product will sell in future months. There are about 107 products that are being bought by different customers for different uses. It is univariate problem since ...
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Time Series:Outlier Detection

I have time series data which looks like the graph mentioned below. I am familiar with the method of removing outliers based on the standard deviation and median values. Drawback of these methods are ...
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Hidden markov model to estimate confidence in binary time series

I have binary time series representing active/inactive states eg. ...
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Out of stock / Spike in demand prediction

The goal is to predict out-of-stock situations, either quantitatively (the gap) or qualitatively (out-of-stock likely to happen in next few weeks). Background: We have existing demand planning ...
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way to add target delay on TimeseriesGenerator from keras.preprocessing.sequence

from deep learning with python book, it created function for data generator. I thought I can do the same using TimeseiresGenerator from keras package but I was not able to add target delay. Is there ...
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Statsmodels - AR model & non-stationary process

I use the $ARMA(n, 0)$ model as $AR(n)$. I noticed that for all polynomial roots we have: $|z_i| > 1$, so it means that the model is non-stationary. Is this possible despite the fact that I use the ...
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How to interpret the graph representing the fit provided by the ARIMA model?

I'm following this tutorial here to build an ARIMA model in R. I've done a Forecast using a fitted model in R. I specified the forecast horizon h periods ahead for predictions to be made and used ...
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1answer
38 views

Time Series Forecasting Seasonal type

As we all know there are two types of seasonal types, additive and multiplicative, but I have trouble telling them apart. To my understanding, in multiplicative seasonality, the magnitude of a ...
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When is it convenient dropping duplicates when performing Time Series Prediction?

I have this Dataset. ...
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1answer
38 views

Is there a Feature selection process for ARIMA model?

I have a dataset representing sales per day for certain products. It contains 30000 observations and 6 features (target included). Since my task is to make a prediction about the number of pieces sold,...
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1answer
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Do we have to split our dataset into training & testing when using ARIMA model?

I am working on a project where I predict the total quantities sold at the ITEM/DAY leve. As for the model, I decided to with an ARIMA model (I'm using R). For guidance, I decided to follow the two ...
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Target Variable Encoding for Time Series Change point detction

I am working on a time series data for which I intend to impliment machine learning model for detecting change point in time series data. This data is recorded fom machinary and we have to predict ...
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1answer
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How to distinguish between normal fluctuation and outliers in ARIMA model?

I have a dataset about sales per day of certain products at the ITEM/DAY/STORE level , I've plotted the series and visually examined it for any outliers, volatility, or irregularities. And this is ...
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Time Series, Point Detection Python

I have time series data generated from the sensor. Now I know the the actual point I need to find is at the 11:50, as you can see in the graph this point dooes not hold any statistical property which ...
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Advice on imputing temperature data with StatsModels MICE

This may be a dumb question but I can't figure out how to actually get the values imputed using StatsModels MICE back into my data. I have a dataframe (dfLocal) with hourly temperature records for ...
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1answer
14 views

Sensorfusion: Generate virtual sensor based on analysis of sensorsdata

I have a steam engine which is equipped with the following sensors: temperature sensor in the boiler room temperature sensor in the heating room pressure sensor in the boiler room rotations-per-...
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How to explain calm period in energy consumption

I have a dataset of energy consumption that looks like this: I got it from Kaggle.com and all it says is that it's energy consumption data from PJM (this is the PJMW_hourly that corresponds to the ...
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1answer
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May I use the same data in several time series intervals?

I am playing with RNNs / LTSMs for a classification task in predicting financial data. I have a time-series going many years back, and are planning to divide it into a number of shorter time-...

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