Questions tagged [forecasting]

Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

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18 views

Seasonal ARIMA- non stationarity after differencing and seasonal differencing

I am working with a seasonal time series, which is initially stationary. After many attempts, the best model that fits the data is an ARIMA(0,1,4)(0,1,1)[12]. However, checking for the stationarity of ...
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15 views

Forecasting time point not value

I have a simple question. when we want to forecast a time series, we always focus on the value of series in future. But could we forecast time point of spesific value? For example I would like to ...
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18 views

Predictive model fusion informed via observed values

Suppose I have some quantity I want to forecast, like the traffic at a particular intersection or the sales volume at a particular store. I have three sources of data to make use of: Broad-scale ...
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17 views

How to Encode Dummy Variables into a Neural Network

I am currently creating a neural network(LSTM) for electrical demand forecasting and I want to include dummy variables to tell the model to treat weekdays differently from weekends, treat working ...
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17 views

dimensionality reduction using SVD for forecasting with machine learning

I'm using a LSTM model to forecast time series data. My dataset has far too many variables and I would like to perform dimensionality reduction. My LSTM model works on a rolling window of 500. I ...
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6 views

Single Variate Fixed Period Lagged Regression

I found a relationship that seems strong, but I'm not finding corroboration of it in research papers, so, am I missing something obvious? I have data (for simplicity of explanation) ...
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1answer
44 views

How can I quantify the impact of the lag between two events that each occurred twice?

There's a question about the impact of holding sales close to one another. Last year, Sale A was held four weeks before Sale B. This year, Sale A was held three weeks before Sale B. How would I go ...
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1answer
73 views

How to predict weekly or monthly sales from daily time series model?

I've been given daily data and I've trained a SARIMAX time series model in Python so that I can predict daily data if given daily input. However, I need to forecast on a monthly or weekly level, ...
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31 views

Training ARIMA based on overlapping hourly weather forecasts

I am working with hourly water level data and I plan to forecast each day the next two weeks (on an hourly base meaning 14*24 = 336 forecasts each day). My regressor is an hourly weather forecast. ...
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36 views

Median-based Versus Average-based forecast? Which is better?

When generating forecasts (e.g., product-customer time series data), should we choose an average-based forecast or median-based forecast? I recently read a very nice article by Nicholas Vandeput on ...
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1answer
48 views

How to interpret straight line as forecasting

I would like to make some short term forecasting using an AR(I)MA model. having the following daily time series, which is for the raw data: It seems to be like a white noise, based on the acf and ...
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23 views

Convert prediction for differenced Time Series ARIMA(1,0,1) and ARIMA (1,1,1)

I am working on a Time Series model, and the series appeared to be non-stationary (presence of trend). I tried 2 ways: 1) put original data into ARIMA(1,1,1) 2) manually difference first order ...
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22 views

How to Choose Error Distribution For Time-Series Model

I am modelling a set of time-series, and understand various models (ARIMA, AR, GARCH) allow for the inclusion of non-Gaussian error distributions. I am aware that, after fitting a time-series model, ...
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29 views

How to Recursively Predict a Time Series Using Neural Networks

I am currently using neural networks to forecast an electrical demand time series. I am trying to create a forecast for the following day given previous observations at half hourly intervals. My ...
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101 views

Is it a valid claim, that by differencing a time series, it loses its memory, and as a result its predictive power?

Marcos Lopez de Prado seems to be a well known and renowned machine learning expert in the field of finance. I am very far from his level, as have not yet finished my PhD in economics, and only have ...
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57 views

Forecasting high-frequency electricity data with multiple seasonalities

for a school project I need to forecast high-frequency data using different methods of my choice. Data: I have hourly data on day-ahead electricity prices and a few other variables (hourly power ...
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1answer
48 views

Do out-of-sample fitting methods solve the problem of over-fitting?

Suppose we have a regression model, and we want to fit this to training data, and then make predictions on test data. There is a well-known danger that out-of-sample predictions will be poor, due to "...
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57 views

Trend & Seasonality Determination in Time Series without looking at Graph [duplicate]

Most of the articles I have read describe determining Trends and Seasonal (TS) effects through rolling your eyes on Graphs. Graph is a nice visual representation, but I am looking a way in either ...
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77 views

Understanding MASE Value

I've looked through many of the other posts concerning the Mean Absolute Scaled Error (MASE) forecast metric and haven't been able to sort out my problem just yet. I'm working with some weather ...
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86 views

Big Mart Sales Prediction Problem

I hope that some of you are familiar with Big Mart sales prediction data that was provided by Analytics Vidhya as a contest. The problem statement of on the website is as follows: The data ...
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Why naive (prediction) forecasting is called random walk?

Why naive (prediction) forecasting is called a random walk? Naive prediction is to use the last value as a forecast. (It's clear that the best prediction for a random walk is a naive one.)
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Why are my neural network predictions so wrong when I add another variable

I have created a neural network in order to predict the following hours electrical demand depending on the previous sixty observations. However I know that temperature affects the load at a given hour ...
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1answer
58 views

What methods are available for forecasting with a sample of the data

In predictive analytics, specifically forecasting, what methods are available for getting the same predictive accuracy with $n$ (a sample of the data) which would be achievable with $N$ (all of the ...
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1answer
66 views

Why aren't my variables correlated?

I am currently doing a project on Load forecasting and it is known that in my country the temperature effects the load. I have hourly readings of Load and Temperature from the period between 01-...
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2answers
33 views

Forecasting a Step-Like Time Series

I have an interesting time series dataset. I have monthly data and I would like to forecast the next 12 months of data points. I know the dates at which the dependent variable 'may' change up or ...
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1answer
24 views

Including regressors to improve forecasts on white noise

I am conducting some time series forecasts using quite limited data, 13 years annually. Basically, I am trying to forecast companies emission totals using historical values. The historical data ...
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1answer
38 views

ARIMA for daily data over 5 years - forecast package

I have a question to auto.arima and seasonality. I have to analyze 39 single datasets which are prices of futures or equities. There are missing data which I replace with na.approx. Then I calculate ...
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1answer
35 views

Predicting n'th percentile [closed]

When we use prediction, we can only say levels. For example: We have 500 sample data for our walking range. And let's say 90 percentile is 16.0 km and 10th percentile is 0.78 km. Well, can only say ...
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1answer
36 views

Combinef in the R HTS package- net aggregation

When using the combinef function in Rob Hyndman's very useful hts library (soon to be in incorporated in the new fable library/tidyverts framework) is it possible to have subtraction at any level? ...
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1answer
21 views

Are conditional mean in an AR(1)-GARCH(1,1) equal for different GARCH(1,1) processes of the same data?

I have created a Markov-Switching GARCH model, where the volatility is defined to be switching between two different GARCH(1,1) processes. The data is assumed to have zero mean, where the data is ...
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9 views

How to forecast the future values ( test data) by making use of the fitted ARIMA model [duplicate]

I am tried as follows But problem is all the fitted values are 39 Data : Test and Train data
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18 views

How do i Remove Differencing applied to a time Series, ARIMA model?

Am trying to forecast using time series method called ARIMA. I have followed steps to build a time series model displayed in the code below. My challenge is on (Merging Actual and Forecast in One ...
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1answer
20 views

When to use Normalized Root-Mean-Squared Error vs Spearman Correlation?

I am doing some Machine Learning experiments with Azure and the graphs that it gives me are measured in Spearman Correlation vs Iteration Number (part of the machine learning) However I was just in ...
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1answer
25 views

Variance of Mean Response at the Mean of the Data

My question concerns the variance of the mean response as outlined in this short article or in this Wikipedia entry. Basically, the variance of the mean response is given by $$\text{Var} \left(\hat{\...
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27 views

Multiple & multi-step time series forecast training data with RNN

I have read a lot of discussion on how to do cross-validation on time series data (e.g. walk forward) but I failed to understand how to properly prepare the training data for multiple time series ...
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1answer
37 views

Fit and Predict Arima in R [closed]

I am trying to fit a Arima model in R with an independent variable (ARIMAX). The model fit data contains both positive and negative numbers. The issue is that after fitting the model, the predict ...
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62 views

Multiple Time series Forecasting Using LSTM in python

Assume I have a m dimensional input feature vector and I would like to perform multiple steps time series forecasting. I have about 500 files which each one is has 100 observations for example ...
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1answer
16 views

How does the sliding window work?

I am not sure how the "Sliding window" method work. Let's assume I have a dataset of number of logins by hour. a) A window of 24hours to predict the next 24h? b) A window of 24h to predict the next ...
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1answer
19 views

Forecasting Process with Limited Historical Data and High Variance

I have a general inquiry regarding a project I am working on. I cannot reveal too much, but I would like to gauge the community here and hopefully be pointed towards the right direction in terms of ...
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2answers
59 views

When forecasting, is it better to remove the outliers or just to transform them?

I am forecasting the number of logins. I have a dataset with the number of logins for each hour. First, I use LOF (local outlier factor) to find the outliers and then I remove them. Second, I use ...
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33 views

Grouped Time Series Forecast when some of the nodes breakdown

I am attempting to do a grouped time series forecast in R using an ARIMA method at the base nodes. However at such a granular level, a few of nodes do not have enough data and so the auto.arima ...
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23 views

Estimating probability density for forecasts

I've used a handful of algorithms for forecasting future values in a time series. But sometimes what I'm really interested in is not the predicted value, but the probability that some future will be ...
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10 views

Timeseries Forecast with log-normalized and differentiated data

i posted a similar, but more confusion question already. I have a weekly timeseries so far, which looks like this (pls ignore the red line): My original data is (e.g.): ...
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35 views

Why am I getting better MAPEs when running an ARIMA model on a non-stationary time series (vs. a stationary one)?

I've been using ARIMA modelling to predict the number of orders a business receives. I have data for 3 years, and the time series shows a strong (uneven) upward trend, with increasing variance over ...
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14 views

Dimension reduction for multivariate spatio-temporal data for hurricanes forecast

I have weather data for the 40 previous years and for each year I have information about the hurricane season (intensity, number of active days, casualties,...). My ultimate goal would be to forecast ...
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2answers
78 views

Removing leading zeros from time series

Currently, I am working with a lot of time series data. A lot of my time series data have a lot of leading zeros. For example, ...
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5 views

How to align predictors with target variable when predictors are sampled at a lower frequency?

I have a set of models I am creating in which the target variable I am forecasting is sampled at a high frequency (daily) but the predictors are all Federal Reserve Macroeconomic variables which are ...
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1answer
41 views

Do we need to stationarize a time series signal when using Kalman filter?

I am working on forecasting the number of logins. I know that before using ARIMA, it is important to remove trend and seasonality. But in the case of Kalman filter, I am not sure. After all it is a ...
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17 views

Using Decomposition to Extrapolate seasonality, cycle and trends of predictors

I'm creating a dynamic regression model in which macroeconomic indicators are predictors/features in the model. I need to forecast these features n-steps into the future. I am planning to decompose ...
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1answer
36 views

How do the forecast intervals from an AR model behave when the time series is inherently stationary?

I'm trying to wrap my head around two contradictory intuitions behind how forecast intervals should behave when we use an AR process to model a stationary time series: (a) On one hand, since the time ...