Facebook prophet is additive model. It contains trend and seasonality. For example, sale forecasting of Facebook prophet is 1200. How do i find the value of seasonality, trend and regressor. For example seasonality components is 300 trend is 500 and regressor is 400. How do i find each value for each components?
model.predict(future) will return a df with trend, seasonality and regressor information on historical data.
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I have time series data and I would like to build an ARIMA forecasting model. I have split my data into train-test. I will be training the model only on the training set and evaluate on testing set.
So my question is when I am plotting the ACF and PACF to get an idea of the appropriate p and q parameters, should I plot the ACF and PACF on my training set or the whole data? and about Auto Arima, should I feed the whole data or just the training set?
I tried with both the training data and the whole data. and they give different results(for both ACF - PACF plots and Auto ARIMA). So which data should I use?
After deseasonalizing using ec_rawhstt=ec_rawhst-ec_rawhst.shift(24)
are those plots valid for determining Ac(p) for autoregression models
sorry, I am a beginner
I have 2 time series, X_t and Y_t, which are on different scales.
Y_t can be 0 to infinite, while X_t is limited to 0 to 100.
How can I determine if the trend of X_t forecast the trend of Y_t? In other words if there is a peak in Xt, then the peak of Yt will follow after some lag.
If this is indeed the case, what is the lag?
I am not interested in forecasting the actual value of Yt.
Using the following chart as an illustration, the red line is Xt (which in my data the values are between 27 to 34), and the black line is Yt (which is about 40000).
I tried to use Time Lagged Pearson Correlation, but I am aware the pearson correlation (of the 2 time series) does not have the concept of time. Pearson correlation simply treats the time series as lists of data.
I have read some guides on Granger causality, but it seems this checks if (the value of) Xt is useful in forecasting the value of Yt, which is similar to a regression framework. (which I am mostly interested in forecasting the trend of Yt)
I am a newbie in time series analysis, Thanks for your time!
I have a series that has a linear trend but no seasonality. I have been trying different time series algorithms. I have tried ARIMA using pmdarima and I get good results with 1st order differencing of the series.
Next, I am using Prophet. With the series as is I get a high MAE. So I differenced the series and used Prophet to make predictions. But now the predicted values (yhat) are the differenced values. How do I convert the predicted values in the yhat column to the original scale so that I can calculate MAE and evaluate the model?
Is it even possible? I have tried all the possible solutions, but since this is unlike min-max scaler, I am not able to find a way out of it. Most of the solutions require the first value of the original series to inverse diff the differenced series.
Any help will be appreciated.
I have ~2.6K hours of sales data with a positive linear trend as well as daily and weekly seasonality. See plotted data. I have tried to model the data using SARIMA and TBATS in python. In both cases, I cannot get the predictions to work as I intend.
For SARIMA, the in sample predictions look great, but when I try to forecast into the future, it looks completely wrong.See here for in sample SARIMA predictionsSee here for how poor the out of sample SARIMA predictions are.
For TBATS, the predicted values match the daily and weekly pattern, but is missing the positive trend, despite me forcing so that use_trend = True. See TBATS model prediction here
I have no idea what I'm doing wrong and have been stuck on this for days! Any advice greatly appreciated.