Questions tagged [forecasting]

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Cross-validation for Timeseries Counterfactual Analysis

We are looking to predict counterfactual states from time-series data. In our problem we are looking to determine the energy savings from a grid-installed device that is varied on and off for many ...
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1answer
29 views

Forecasting multiple time series with a single model

I have a dataset with sales numbers for ~500 different markets (assume different cities or regions) and need monthly forecasts for each market. Instead of building 500 different models, I'm interested ...
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12 views

Why might an LSTM be capable of predicting an ARMA signal but not a linear combination of ARMA signals?

I have an LSTM network and am testing it on some dummy ARMA signals. I'm trying to predict the signal 5 time steps into the future. The network is capable of outperforming Naive (persistence) when ...
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13 views

For time series forecasting task, should I use data across several time steps or singe timing data for prediction?

I have a time series forecasting project, there are over 10, 000 time steps of data, so the data amount is not a problem. At first, I thought I've to create a time-based data pipeline that forms the ...
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5 views

How is possible the result of GRU would other way around compared to LSTM while they were implemented samely?

Recently I crossed to a situation I can't figure it out why it happened. I applied six predictive models on a specific dataset as training-set and tried to predict the other similar dataset as an ...
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1answer
37 views

LSTM with input of actual time step

I'm working on an implementation of LSTM neural network to forecast energy consumption. I have a dataset with load, series of weather parameters and indicator of it's bank holiday or not. I first ...
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1answer
36 views

How to make forecast in LSTM with specific/defined feature values

I am analyzing the avocado dataset to predict the future prices of avocado depending on the region and type (organic/conventional). I've trained my model which seems to be working. The test results ...
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0answers
16 views

How to time series forecast with multiple time series data sets on the same time series index

How does time series work with multiple time series data sets on the same index? For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...
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17 views

How to get rid off differences between prediction and actuals while predicting or forecasting time series data

I have performed Boxcox transformation on my time series data and processed it through ARIMA modeling. Converted prediction values to the actual. I see significant differences between actual and ...
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9 views

What kind of feature engineering suits for random generation like time series data

My time series data looks like random generation which was obtained by aggregation day-wise sales of a product. The dependent variable does not show any kind of pattern. So have tried tries different ...
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1answer
22 views

How to forecast product/item sales next one week using Xgboost regressor

I have trained xgboost algorithm to predict the number of items sale on a given day and got pretty good results, now I would like to forecast sales ahead of one week. I tried re-training the ...
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0answers
26 views

Is it possible to use LSTM for time series forecasting for future months with test data having only NaN values?

my dataset is a univariate time series with one column as months, other column having demands for the corresponding months. My test dataset has NaN vals only for all the months. Can LSTM be used in ...
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1answer
40 views

How to split a dataset into train and test sets for time series (multiple step-multiple output forecasting)?

I am trying to use a LSTM neural net to do multiple step / multiple output forecasting (I predict multiple values in one time knowing some values in the past). But, I have realized that I must be ...
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0answers
10 views

How to get quantiles/probabilities of time series forecasts?

my problem is as follows : I am creating demand forecasts for some goods with different methods (ARIMA, ETS,..) The issue is that I would like to forecast the probabilities of those forecasts since ...
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0answers
9 views

What's the meaning of Err in Online Time Series Predicion with Missing Values

I'm reading THIS paper about online predictions on time series with missing values. And trying to code the third algorithm in C++. The thing is that I don’t understand what they mean by $Err_{\tau}...
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1answer
24 views

Building a time-series demand forecasting model

I am forecasting demand for certain types of goods and services, which I expect to be correlated to a sub-set of a basket of macroeconomic indicators (considering 15-20 indicators) I do not know ...
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0answers
25 views

Training multiple multivariate time series

I have just learned LSTM for one month, and I am doing a project that aims to train an LSTM model forecasting the taxi demand at "t+1" according to the taxi demand at "t", "t-1"... In particular, I am ...
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0answers
18 views

How to Manipulate date data for multiple visits per person?

I have a query to solve. I have data regarding customers and number of visits done to them. These are in two tables. So I want to join two table and create different features so that I can find better/...
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0answers
15 views

Finding p values and estimates of external variables in facebook Prophet for forecasting in Python

I am using facebook Prophet for multivariate forecasting which has an objecting of forecasting prices. My target variable is affected by n number of external variables too. I am using add_regressor ...
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0answers
13 views

A way to identify anomalous trends amongst several trends?

I'm working on a personal project, where I'd like to identify anomalous trends. Here's the scenario: Imagine a company can sell 3 types of say, candies: X, Y, and Z. For some reason, these prices can ...
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9 views

ARCH and GARCH model working and R code for its implementation

my time series data set doesn't have a proper trend or seasonality. Heard about ARCH and GARCH model which works fine on Un-trended data set but I am not finding the proper R code for its ...
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2answers
59 views

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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0answers
25 views

Generalization of RNN/LSTM/GRU… model

Given a time-series prediction with a Recurrent Neural Network (doesn't matter if LSTM/GRU/...), a forecast might look like this: to_predict (orange) was fed to the model, predicted (purple) is the ...
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2answers
69 views

What arguments should I pass to input_shape parameter of LSTM function in Keras?

My dataset has 2944424 rows and 6 columns. I am using an LSTM in Keras to forecast taxi demand. I am having problem with the input_shape parameter of the LSTM. It ...
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0answers
20 views

What approach should I take to model forecasting problem in machine learning?

I have a dataset which contains 4000k rows and 6 columns. The goal is to predict travel time demand of a taxi. I have read many articles regarding how to approach the problem. So, every writer tell ...
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0answers
8 views

What is forecast horizon measured in?

I am using Microsoft Azure machine learning platform and when using the forecast feature it gives me the field of forecast horizon that uses an integer. Does the integer mean days, weeks, years, etc. ...
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1answer
44 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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1answer
54 views

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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0answers
18 views

Finding Outliers in Resource Forecast Data

Unsure if this is the correct place to place this, please close if so. I'm a workforce analyst at a large retail company, I own and maintain all the forecasting for our retail stores. This is based ...
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1answer
125 views

How to predict NaN (missing values) of a dataframe using ARIMA in Python?

I have a dataframe df_train of shape (11808, 1) that looks as follows: ...
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0answers
14 views

Compute error for each point the LSTM predict / epoch

I'd like to compute the error of each point my LSTM predicted per epoch. Example for 6 points predicted : epoch 1 : RMSE = [4,1,9,3,4,5] epoch 2 : RMSE = [2,0,6,5,3,6] etc. So at the end I can ...
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0answers
12 views

Standard for aligning data to week based on Mon to Sun for forecasting

I need to produce a forecast where i have predicted volume to a standard definition of a week. This definition is simply the week starts on Monday and Ends on Sunday. Is there an ISO standard that ...
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0answers
22 views

MAPE as an accuracy measure

I want to run and compare time-series forecast methods. Mean Absolute Squared Error (MAPE) is considered one of the strongest metrics for accuracy. My question is the following: If you do $1-MAPE$ ...
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0answers
18 views

if a time series is not stationary at a weekly level, is it also not stationary at quarterly level?

I have time series of sales of many products on weekly level for 2 years. I am interested in forecasting the sales on quarterly (4-months) level for every product. I also have some exogenous ...
2
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1answer
94 views

Time Series Forecasting for Multiple Customers using one RNN

I have a product which has univariate and also multivariate time series data from multiple customers. I have variable amount of data available. Ranging between couple of years to couple of months. ...
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0answers
88 views

How to apply an RNN to forecast non-stationary time series?

Is it possible to predict a time series which is non-stationary, in the sense that, the dependent variable Y have an increasing trend? Therefore, the highest value of $Y$ in the training set may be ...
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1answer
25 views

In which CRAN mirror is the quadprog package available?

I was trying to find the accuracy of some predicted data and I came to know that I can use the forecast package to do this. When I installed it and gave library(forecast), it said it needs quadprog ...
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50 views

Dummy/baseline models for time series forecasting

I am working on an evaluation of time series forecasting models in Python, more specifically with statsmodels, scikit-learn and <...
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0answers
65 views

predictive clustering trees in Python?

I am faced with a time series forecasting cold-start problem, specifically I am forecasting energy consumption of businesses where historic consumption data is available only for training but not new ...
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1answer
28 views

How to forecast time series analysis for more then one dependent variables?

I have three datasets: Dataset_1= column name as ID, Date, counter_id Dataset_2= column name as ID, no. of tickets sold ...
1
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1answer
162 views

Training a LSTM on a time serie containing multiple inputs for each timestep

I am trying to train a LSTM in order to use it for forecasting : the problem is basically a multivariate multi-steps time series problem. It is simply an experiment to see how statistical models (...
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2answers
61 views

Time-series forecasting

Here is the data: ...
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1answer
67 views

Training data : forecasted or actual?

I am working on a time series prediction problem. I am using keras models for machine learning. For this prediction, weather variables are used as input. They can be of two types: forecasted and ...
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0answers
38 views

What's the difference between ELM and NNAR?

I'm working with time series forecasting using the two techniques that involve neural networks, the Extreme Learning Machine and the Autoregressive Neural Network. Reading the two methodologies, the ...
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0answers
29 views

Can I forecast with discontinued data using ARIMA?

I have data for sales on monthly basis, but a few months' information is not in the CSV file or data file. Can I forecast or fill that missing month with other calculated values from present records? ...
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0answers
64 views

How to model non-linear demand function?

I am trying to build a dynamic pricing algorithm on intermittent data (a lot of zeros between non-zero values). I have on average 100 non-zero data points for each product. However, it seems to be ...
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1answer
72 views

What Predictions can be done for Restroom/Washroom Data? [closed]

My company has installed sensors, which actually monitors the Restrooms/Washrooms in the Building. Currently the sensors collect data such as Ammonia, Nitrous and Visitors Count; All the data ...
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0answers
16 views

How can I enrich train data in case of cnn using target and time features

I have a sequence of images, let's say we ignore time specificity for now. In the other hand, target is a multivariate continuous time series. Let's consider it just a univariate one. Training a cnn ...
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0answers
47 views

LSTM - Forecasting usage (real world) [closed]

I see that LSTM is very powerful reconstructing time series it was fed with, but my issue is: => Can LSTM predict future values without requiring real (training/testing) data? My objective is simple:...
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0answers
33 views

forecast product demand in one week using machine learning approach

I'm trying to predict product demand in store. The predictors I have include price, competitor's price, store ID, date. My target variable is sales volume (in a particular store). What I need to ...